Search

In the growing field of the Internet of Medical Things (IoMT), securing the transmission of medical images over public networks is a critical challenge. Medical images, being highly sensitive and often containing personally identifiable information, require robust protection against unauthorized access and tampering. This paper addresses this challenge by introducing a novel cryptosystem specifically tailored to the resource limitations inherent in IoMT environments. To meet the demand for protecting sensitive information in medical images, the proposed system integrates two layers of security: spatial watermarking and asymmetric encryption. At the core of our approach lies a newly developed, cost-effective spatial watermarking algorithm that seamlessly embeds watermarks within host images to facilitate tamper detection. Complementing this, we employ a resource-efficient Twin Message Fusion (TMF) encryption scheme to ensure confidentiality and integrity during transmission. The proposed cryptosystem is evaluated for both watermarking effectiveness and encryption robustness. Additionally, we assess the system's suitability for IoMT environments in terms of cost-effectiveness. Mean Squared Error (MSE) and Peak Signal-to-Noise Ratio (PSNR) metrics have been used to assess watermarking resilience against various attacks, while encryption efficacy is analyzed through histogram and entropy evaluations. Furthermore, we compare the complexity of our cryptosystem with five recently proposed techniques in both traditional and IoMT environments, demonstrating superior execution time, reduced image size, and enhanced encryption efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the rapid integration of Internet of Things (IoT) devices into the healthcare sector has brought about revolutionary advancements in patient care and data management. While these technological innovations hold immense promise, they concurrently raise critical security concerns, particularly in safeguarding medical data against potential cyber threats. The sensitive nature of health-related information requires robust measures to ensure patient data's confidentiality, integrity, and availability within IoT-enabled medical environments. Addressing the imperative need for enhanced security in IoT-based healthcare systems, we propose a comprehensive method encompassing three distinct phases. In the first phase, we implement blockchain-enabled request and transaction encryption to fortify the security of data transactions, providing an immutable and transparent framework. Subsequently, in the second phase, we introduce request pattern recognition check, leveraging diverse data sources to identify and thwart potential unauthorized access attempts. Finally, the third phase incorporates feature selection and the BiLSTM network to enhance the accuracy and efficiency of intrusion detection through advanced machine-learning techniques. We compared the simulation results of the proposed method with three recent related methods, namely AIBPSF-IoMT, OMLIDS-PBIoT, and AIMMFIDS. The evaluation criteria encompass detection rates, false alarm rates, precision, recall, and accuracy, crucial benchmarks in assessing the overall performance of intrusion detection systems. Notably, our findings reveal that the proposed method outperforms these existing methods across all evaluated criteria, underscoring its superiority in enhancing the security posture of IoT-based healthcare systems. [ABSTRACT FROM AUTHOR]

In distributed computing, data trading mechanisms are essential for ensuring the sharing of data across multiple computing nodes. Nevertheless, they currently encounter considerable obstacles, including low accuracy in matching trading parties, ensuring fairness in transactions, and safeguarding data privacy throughout the trading process. In order to address these issues, we put forward a data trading security scheme based on zero-knowledge proofs and smart contracts. In the phase of preparing the security parameters, the objective is to reduce the complexity of generating non-interactive zero-knowledge proofs and to enhance the efficiency of data trading. In the pre-trading phase, we devise attribute atomic matching smart contracts based on precise data property alignment, with the objective of achieving fine-grained matching of data attributes between trading parties. In the trading execution phase, lightweight cryptographic algorithms based on elliptic curve cryptography (ECC) and non-interactive zero-knowledge proofs are employed for the dual encryption of trading data and the generation of attribute proof contracts, thus ensuring the security and privacy of the data. The results of experiments conducted on the Ethereum platform in an industrial IoT scenario demonstrate that our scheme maintains stable and low-cost consumption while ensuring accuracy in matching and privacy protection. [ABSTRACT FROM AUTHOR]

The sharing of electronic medical records (EMRs) is of immense value for disease research and epidemic prevention. It has received considerable attention from researchers. EMR sharing and management which based on blockchain have demonstrated remarkable achievement. Nevertheless, there remain certain obstacles that necessitate attention and resolution. Most blockchain-based eHealth systems store EMRs on outsourcing companies. The outsourcing companies may leak electronic medical records. Moreover, patients cannot decide who can access their electronic medical records and lose control of their EMR completely. To solve these problems, we propose a patient-centered EMR sharing and management scheme based on blockchain with proxy re-encryption algorithm to protect the interests of the patient and use smart contracts to achieve patients control on their electronic medical records. Moreover, a decentralized proxy that re-encrypts the electronic medical records is adopted to improve the security and availability of our system. Furthermore, we use an architecture to compute the hash root with chameleon hash to implement additional features such as electronic medical record modification, emergency access, and access revocation. To evaluate our scheme, our scheme is deployed on Hyperledger Fabric. The results which published by Hyperledger Caliper show the successful performance of our scheme in achieving security and privacy requirements. [ABSTRACT FROM AUTHOR]

Data Mining has been widely applied in many areas such as scientific analysis, healthcare, finance and marketing. Association rule mining is one of the broadly used methods in data mining applications. In recent years, with the rapid growth of cloud computing, many academic solutions to the outsourced privacy-preserving association rule mining (PPARM) problem have been proposed under various threat models. However, existing approaches cannot simultaneously satisfy the requirements of strong security and high efficiency. In this paper, we focus on effectively solving the PPARM problem over semantically secure ciphertexts. In particular, we propose a novel scheme which executes Apriori algorithm on outsourced encrypted databases. The proposed scheme achieves database security, preserves mining privacy and supports off-line clients during the mining process. To the best of our knowledge, our work is the first to design a semantically secure PPARM scheme without on-line users. Also, we provide formal security analysis and conduct extensive experiments to evaluate the efficiency of our proposed scheme. The results indicate that our scheme can be efficiently implemented on large databases. [ABSTRACT FROM AUTHOR]

Federated learning enables training healthcare diagnostic models across multiple decentralized devices containing local private health data samples, without transferring data to a central server, providing privacy‐preserving services for healthcare professionals. However, for a model of a specific field, some medical data from non‐target participants may be included in model training, compromising model accuracy. Moreover, diagnostic queries for healthcare models stored in cloud servers may result in the leakage of the privacy of healthcare participants and the parameters of models. Furthermore, the records of model searching and usage could be tracked causing privacy disclosure risk. To address these issues, we propose a blockchain‐based privacy‐preserving searchable attribute‐based encryption scheme for the diagnostic model federated learning in the Internet of Medical Things (BSAEM‐FL). We first adopt fine‐grained model trainer participation policies for federated learning, using the attribute‐based encryption (ABE) mechanism, to realize model accuracy and local data privacy. Then, We employ searchable encryption technology for model training and usage to protect the security of models stored in the cloud server. Blockchain is utilized to implement distributed healthcare models' keyword‐based search and model users' attribute‐based authentication. Lastly, we transfer most of the computational overhead of user terminals in model searching and decryption to edge nodes, achieving lightweight computation of IoMT terminals. The security analysis proves the security of the proposed healthcare scheme. The performance evaluation indicates our scheme is of better feasibility, efficiency, and decentralization. [ABSTRACT FROM AUTHOR]

The development of Intelligent Railway Transportation Systems necessitates incorporating privacy-preserving mechanisms into AI models to protect sensitive information and enhance system efficiency. Federated learning offers a promising solution by allowing multiple clients to train models collaboratively without sharing private data. However, despite its privacy benefits, federated learning systems are vulnerable to poisoning attacks, where adversaries alter local model parameters on compromised clients and send malicious updates to the server, potentially compromising the global model's accuracy. In this study, we introduce PMM (Perturbation coefficient Multiplied by Maximum value), a new poisoning attack method that perturbs model updates layer by layer, demonstrating the threat of poisoning attacks faced by federated learning. Extensive experiments across three distinct datasets have demonstrated PMM's ability to significantly reduce the global model's accuracy. Additionally, we propose an effective defense method, namely CLBL (Cluster Layer By Layer). Experiment results on three datasets have confirmed CLBL's effectiveness. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models. [ABSTRACT FROM AUTHOR]

As online social networks (OSNs) become more prevalent, a new paradigm for problem-solving through crowd-sourcing has emerged. By leveraging the OSN platforms, users can post a problem to be solved and then form a team to collaborate and solve the problem. A common concern in OSNs is how to form effective collaborative teams, as various tasks are completed through online collaborative networks. A team's diversity in expertise has received high attention to producing high team performance in developing team formation (TF) algorithms. However, the effect of team diversity on performance under different types of tasks has not been extensively studied. Another important issue is how to balance the need to preserve individuals' privacy with the need to maximize performance through active collaboration, as these two goals may conflict with each other. This research has not been actively studied in the literature. In this work, we develop a TF algorithm in the context of OSNs that can maximize team performance and preserve team members' privacy under different types of tasks. Our proposed PRivAcy-Diversity-Aware TF framework, called PRADA-TF, is based on trust relationships between users in OSNs where trust is measured based on a user's expertise and privacy preference levels. The PRADA-TF algorithm considers the team members' domain expertise, privacy preferences, and the team's expertise diversity in the process of TF. Our approach employs game-theoretic principles Mechanism Design to motivate self-interested individuals within a TF context, positioning the mechanism designer as the pivotal team leader responsible for assembling the team. We use two real-world datasets (i.e., Netscience and IMDb) to generate different semi-synthetic datasets for constructing trust networks using a belief model (i.e., Subjective Logic) and identifying trustworthy users as candidate team members. We evaluate the effectiveness of our proposed PRADA-TF scheme in four variants against three baseline methods in the literature. Our analysis focuses on three performance metrics for studying OSNs: social welfare, privacy loss, and team diversity. [ABSTRACT FROM AUTHOR]

In the Internet of Things (IoT), the selection of mobile users with IoT-enabled devices plays a crucial role in ensuring the efficiency and accuracy of data collection. The reputation of these mobile users is a key indicator in selecting high-quality participants, as it directly reflects the reliability of the data they submit and their past performance. However, existing approaches often rely on a trusted centralized server, which can lead to single points of failure and increased vulnerability to attacks. Additionally, they may not adequately address the potential manipulation of reputation scores by malicious entities, leading to unreliable and potentially compromised user selection. To address these challenges, we propose PRUS, a privacy-preserving and quality-aware user selection scheme for IoT. By leveraging the decentralized and immutable nature of the blockchain, PRUS enhances the reliability of the user selection process. The scheme utilizes a public-key cryptosystem with distributed decryption to protect the privacy of users' data and reputation, while truth discovery techniques are employed to ensure the accuracy of the collected data. Furthermore, a privacy-preserving verification algorithm using reputation commitment is developed to safeguard against the malicious tampering of reputation scores. Finally, the Dirichlet distribution is used to predict future reputation values, further improving the robustness of the selection process. Security analysis demonstrates that PRUS effectively protects user privacy, and experimental results indicate that the scheme offers significant advantages in terms of communication and computational efficiency. [ABSTRACT FROM AUTHOR]

KubeEdge is an open-source system extending native containerized application orchestration and device management to hosts at the edge. One of the main disadvantages of edge computing is the lack of an effective resource allocation and privacy-preserving framework. The objective of this study dynamic task offloading is an important concept in resource allocation and privacy-preserving framework in Kubeedge-based edge computing using machine learning. This research focuses on enhancing the efficiency and security of edge computing within the framework of IoT devices within a KubeEdge cluster. It begins by gathering crucial data on computational power, memory, and network bandwidth from IoT devices, which is essential for informed decision-making. The study evaluates KubeEdge in terms of computational resource distribution and delay, introducing a privacy-preserving multi-layer Blockchain-enabled data aggregation mechanism. This approach ensures efficient data storage with adaptable and reliable data access control. The research addresses privacy concerns in IoT applications, balancing information loss and disclosure risk, and highlights the impact of forwarding traffic on cluster throughput and service delays in edge computing environments. Dynamic resource allocation algorithms are employed, considering profiling data and machine learning models for real-time task offloading decisions, guaranteeing sufficient allocated resources. A Multi-Agent Collaborative-Reinforcement Learning with Salp Swarm Algorithm is proposed for resource allocation in the edge computing environment, enhancing resource efficiency and secrecy performance. Blockchain ensures secure and transparent transactions, reinforcing trust in the dynamic edge environment. Reinforcement learning optimizes task offloading decisions, adapting to changing conditions. Auction games introduce a competitive mechanism, enhancing efficiency in resource allocation. When combined, they create a robust framework where blockchain guarantees data integrity, reinforcement learning optimizes resource usage, and auction games introduce a fair and efficient task allocation mechanism, ensuring both performance and privacy in computing environments. Furthermore, a Partitioning-Dynamic Collaborative energy-aware task offloading scheme is developed to securely offload tasks while preserving data privacy, enhancing trust computing and task offloading capabilities. The study also proposes a Hybrid Greedy Randomized Adaptive Stackelberg-Auction Game Approach to optimize offloading performance, reduce information loss, and decrease time consumption. This comprehensive framework includes task scheduling to reduce energy consumption, enhance privacy, and security, and reduce latency. The proposed work is analysed based on the Matlab software, and it shows a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network. Continuous performance evaluation of resource utilization, response time, and privacy protection further improves offloading decision accuracy over time. [ABSTRACT FROM AUTHOR]

The era of digitization and IoT devices is marked by the constant storage of massive amounts of data. The growing adoption of smart home environments, which use sensors and devices to monitor and control various aspects of daily life, underscores the need for effective privacy and security measures. HE is a technology that enables computations on encrypted data, preserving confidentiality. As a result, researchers have developed methodologies to protect user information, and HE is one of the technologies that make it possible to perform computations directly on encrypted data and produce results using this encrypted information. Thus, this research study compares the performance of three ML models, XGBoost, Random Forest, and Decision Classifier, on a real-world smart home dataset using both with and without FHE. Practical results demonstrate that the Decision Classifier showed remarkable results, maintaining high accuracy with FHE and even surpassing its plaintext performance, suggesting that encryption can enhance model accuracy under certain conditions. Additionally, Random Forest showed efficiency in terms of execution time and low prediction errors with FHE, making it a strong candidate for encrypted data processing in smart homes. These findings highlight the potential of FHE to set new privacy standards, advancing secure and privacy-preserving technologies in smart environments. [ABSTRACT FROM AUTHOR]

Palmprint as biometrics has gained increasing attention recently due to its discriminative ability and robustness. However, existing methods mainly improve palmprint verification within one spectrum, which is challenging to verify across different spectrums. Additionally, in distributed server-client-based deployment, palmprint verification systems predominantly necessitate clients to transmit private data for model training on the centralized server, thereby engendering privacy apprehensions. To alleviate the above issues, in this paper, we propose a physics-driven spectrum-consistent federated learning method for palmprint verification, dubbed as PSFed-Palm. PSFed-Palm draws upon the inherent physical properties of distinct wavelength spectrums, wherein images acquired under similar wavelengths display heightened resemblances. Our approach first partitions clients into short- and long-spectrum groups according to the wavelength range of their local spectrum images. Subsequently, we introduce anchor models for short- and long-spectrum, which constrain the optimization directions of local models associated with long- and short-spectrum images. Specifically, a spectrum-consistent loss that enforces the model parameters and feature representation to align with their corresponding anchor models is designed. Finally, we impose constraints on the local models to ensure their consistency with the global model, effectively preventing model drift. This measure guarantees spectrum consistency while protecting data privacy, as there is no need to share local data. Extensive experiments are conducted to validate the efficacy of our proposed PSFed-Palm approach. The proposed PSFed-Palm demonstrates compelling performance despite only a limited number of training data. The codes have been released at https://github.com/Zi-YuanYang/PSFed-Palm. [ABSTRACT FROM AUTHOR]

In the contemporary era, online social networks have become the prevalent medium for interpersonal interactions, encompassing a multitude of virtual social relationships. To prevent attackers from inferring more sensitive information through social relationships, numerous researchers have devised privacy protection methodologies tailored to these social relationships. However, most of research achievements have tended to overlook considerations pertaining to algorithmic efficiency and the delicate balance between privacy and utility. To address this issue, we first identify vulnerable nodes within social networks using two indicators, namely, the Marchenko–Pastur law rate and the mean spectral radius. This approach aims to enhance algorithm efficiency. Furthermore, we propose a privacy-preserving algorithm based on vulnerable nodes, PPVN, which classifies the friendship links associated with vulnerable nodes into three distinct levels, thereby ensuring precise safeguarding of social relationship privacy while striking an optimal equilibrium between privacy and structural utility. To bolster privacy safeguards, we develop a replacement index θ, designed explicitly to preclude replaced friendship links from evolving into newly sensitive links. Empirical findings substantiate the remarkable efficacy of the PPVN algorithm in preserving user privacy while concurrently upholding data utility. Compared with other privacy-preserving methods, the PPVN algorithm demonstrates superior privacy protection within the shortest computational timeframe. [ABSTRACT FROM AUTHOR]

This article centers around the privacy‐preserving‐based secure H∞$$ {H}_{\infty } $$ filtering issue for interval type‐2 (IT‐2) fuzzy networked systems with false data injection (FDI) attacks. In order to achieve the goal of privacy preserving and significantly enhancing system security against potential eavesdropping threats, a novel encryption‐decryption mechanism (EDM) is adopted to safeguard the safety of signals across the network. The mechanism encrypts the transmitted signal by introducing artificial noise, secret key, and utilizing randomly selected nodes. This ensures that the actual transmitted data remains invisible to eavesdroppers while minimally the impact on the estimated performance of the proposed EDM. Given the network communication resources are becoming constrained due to the ever‐increasing network traffic, an adaptive event‐triggered mechanism (AETM) is employed to ease network congestion by an adaptively adjustable threshold. Then, various sufficient conditions have been outlined to ensure that the filtering error system meets the prescribed disturbance attenuation level. In the end, a numerical example is presented to evaluate both the precision and effectiveness of the developed algorithms. [ABSTRACT FROM AUTHOR]

The electromagnetic spectrum is a limited resource. With the widespread application of the electromagnetic spectrum in various fields, the contradiction between the demand for the electromagnetic spectrum and electromagnetic spectrum resources has become increasingly prominent. Spectrum sharing is an effective way to improve the utilization of the electromagnetic spectrum. However, there are many challenges in existing distributed electromagnetic spectrum trading based on blockchain technology. Since a blockchain does not provide privacy protection, the risk of privacy leakage during the trading process makes electromagnetic spectrum owners unwilling to share. In addition, a blockchain only guarantees integrity, and the imperfect trading dispute resolution mechanism causes electromagnetic spectrum owners to be afraid to share. Therefore, we propose a privacy-preserving electromagnetic-spectrum-sharing trading scheme based on blockchain and ABE. The scheme not only designs an ABE fine-grained access control model in ciphertext form but also constructs a re-encryption algorithm that supports trading arbitration to achieve privacy protection for electromagnetic spectrum trading. Finally, we experimentally evaluated the efficiency of the proposed electromagnetic spectrum trading scheme. The experimental results show that the electromagnetic spectrum trading scheme we propose was highly efficient. [ABSTRACT FROM AUTHOR]

Summary: In person‐centric applications, the prevalence of shared names significantly hampers document retrieval, web search, and database integration, highlighting the critical need for name disambiguation. Network embedding based unsupervised name disambiguation methods have received much attention due to their wide applicability and superior disambiguation performance. However, existing methods require complex feature engineering, such as extracting biographical and source content features or constructing supplementary features from external knowledge bases, which are unavailable in privacy‐preserving scenarios. Moreover, they may face challenges such as imbalanced node training or overlooking global statistical information during node embedding learning. In this article, we propose a method to tackle the name disambiguation problem based on high‐degree penalty and global statistical information using only relational data. First, we construct a weighted source similarity network based on multi‐hop collaborator relationships. Second, we employ a high‐degree penalty based global statistical network embedding model to learn low‐dimensional node embeddings and preserve the structural features of the network. Finally, we cluster the same sources using a joint clustering algorithm that does not require prior knowledge of the number of clusters. The experiment validates the effectiveness of the proposed method on two real‐life datasets. [ABSTRACT FROM AUTHOR]

With the speedy growth of the populace, corresponding waste also grows, notably raising greater concern about the monitoring of waste in urban areas. Although the traditional method of waste monitoring has benefited city waste management systems, it remains time‐consuming, inefficient, and detrimental to public health. This situation can be improved by integrating advanced technologies, which can significantly enhance the flexibility and performance of waste monitoring systems. However, these advanced technologies encounter numerous security challenges. In this article, we have implemented a signature‐based monitoring approach to oversee the status of smart bins. To ensure the security, robustness, and reliability of the introduced protocol, we utilized tools such as AVISPA, Scyther, the ROR model, and Mao and Boyd logic. As discussed in the informal security analysis, the introduced protocol successfully fulfills the security requirements for smart waste monitoring systems. Finally, an in‐depth comparative analysis of the related works and the introduced protocol shows that the latter provides greater security, reliability, and efficiency. As a result, the reliability and hardness of the cryptographic operations used in the introduced protocol make it secure enough to use in real‐world applications. [ABSTRACT FROM AUTHOR]

This systematic literature review examines the implementation and analysis of zk‐SNARK, zk‐STARK, and bulletproof non‐interactive zero‐knowledge proof (NIZKP) protocols in privacy‐preserving applications across diverse sectors. Examining 41 research works obtained through the systematic search queries and filtering criteria published from 2015 to April 2023, we categorized findings into financial, medical, business, general, and other domains. Our analysis highlights significant variations of up to several orders of magnitude in real‐world performance across implementations utilizing NIZKP protocols. However, divergent methodologies in security analyses hindered conclusive comparisons. Addressing research gaps, our future endeavors aim to establish a real‐world benchmark for these protocols. [ABSTRACT FROM AUTHOR]

Web 3.0 makes crowdsensing services more popular, because of its decentralisation and interoperability. Lost Object Finding (LOF) in vehicular crowdsensing is an emerging paradigm in which vehicles act as detectors to find lost objects for their owners. To enjoy LOF services, object owners need to submit the tag ID of his lost object, and then detectors need to update their detecting results together with their locations. But the identity and location information are usually sensitive, which can be used to infer the locations of lost objects, or track participant detectors. This raises serious privacy concerns. In this paper, we study the privacy leakages associated with object finding, and propose a privacy-preserving scheme, named pFind, for locating lost objects. This scheme allows owners to retrieve the locations of their lost objects and provides strong privacy protection for the object owners, lost objects, and detectors. In pFind, we design an oblivious object detection protocol by using RBS cryptosystem, which simultaneously provides confidentiality, authentication and integrity for lost objects detection. Meanwhile, we propose a private location retrieval protocol to compute the approximate location of a lost object over encrypted data. We further propose two optimizations for pFind to enhance functionality and performance. Theoretical analysis and experimental evaluations show that pFind is secure, accurate and efficient. [ABSTRACT FROM AUTHOR]

While it is well known that privacy-preserving cox regression generally consists of a semi-honest cloud service provider (CSP) who performs curious-but-honest computations on ciphertexts to train the cox model. No one can verify the behaviors of CSP when he performs computations dishonestly in reality. Focusing on this problem, we propose a verifiable privacy-preserving cox regression algorithm tailored with the semi-malicious CSP, where all his behaviors are recorded on a witness tape fulfilling the requirement of transparency. To be specific, a multi-key fully homomorphic encryption (FHE) is used to protect the information of different data owners. The verifiability of our proposed multi-key homomorphic message authenticator (HMAC) ensures CSP sends correct results back to data owners. Furthermore, the compactness of FHE and succinctness of HMAC both under multi keys make the cox regression scheme more feasible. The efficiency of our proposed cox regression scheme is also proved by both theoretical analyses and experimental evaluations. After 21 iterations, it costs no more than 10 min to evaluate our cox regression scheme. [ABSTRACT FROM AUTHOR]

The progress of machine learning has revealed its immense potential in emerging medical diagnosis application. This application enables medical users to access diagnosis service that utilizes machine learning models held by service providers. However, the protection of user data and models has emerged as a significant concern, leading to the proposal of numerous privacy-preserving medical diagnosis schemes. Unfortunately, many of these schemes rely on heavy cryptographic primitives like homomorphic encryption, resulting in lengthy diagnosis processes. To address this issue, we present a novel privacy-preserving medical diagnosis scheme that leverages Gaussian kernel-based support vector machine and employs the use of a lightweight primitive known as additive secret sharing. We design protocols for secure decision function computation and secure sign function computation, solving both two-class and multi-class medical diagnosis problems. Furthermore, our solution is generic and applicable to various scenarios that involve the Gaussian kernel-based support vector machine. To validate our scheme, we conduct evaluations on six real medical datasets. And the results demonstrate that our privacy-preserving medical diagnosis approach yields more accurate and efficient outcomes. Specifically, it achieves an accuracy of 99.09% on the dermatology dataset, surpassing the existing schemes using linear support vector machine. In addition, it takes 133ms on the breast cancer dataset, which is significantly faster than the schemes based on homomorphic encryption. [ABSTRACT FROM AUTHOR]

The article focuses on the challenge of conducting effective Privacy-Preserving Cross-Domain Recommendation (PPCDR) due to rising privacy concerns and the limited success of existing models in capturing heterogeneity across multiple domains. It mentions the proposed PPCDR approach introduces a federated graph learning method that combines global and local user preferences across domains while ensuring privacy through differential privacy techniques.

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic factors associated with specific traits. However, ethical constraints prevent the direct exchange of genetic information, prompting the need for privacy preservation solutions. To address these issues, earlier works are based on cryptographic mechanisms such as homomorphic encryption, secure multi-party computing, and differential privacy. Very recently, federated learning has emerged as a promising solution for enabling secure and collaborative GWAS computations. This work provides an extensive overview of existing methods for GWAS privacy preserving, with the main focus on collaborative and distributed approaches. This survey provides a comprehensive analysis of the challenges faced by existing methods, their limitations, and insights into designing efficient solutions. [ABSTRACT FROM AUTHOR]

Smart healthcare systems promise significant benefits for fast and accurate medical decisions. However, working with personal health data presents new privacy issues and constraints that must be solved from a cybersecurity perspective. Edge intelligence-enabled federated learning is a new scheme that utilises decentralised computing that allows data analytics to be carried out at the edge of a network, enhancing data privacy. However, this scheme suffers from privacy attacks, including inference, free-riding, and man-in-the-middle attacks, especially with serverless computing for allocating resources to user needs. Edge intelligence-enabled federated learning requires client data insertion and deletion to authenticate genuine clients and a serverless computing capability to ensure the security of collaborative machine learning models. This work introduces a serverless privacy edge intelligence-based federated learning (SPEI-FL) framework to address these issues. SPEI-FL includes a federated edge aggregator and authentication method to improve the data privacy of federated learning and allow client adaptation and removal without impacting the overall learning processes. It also can classify intruders through serverless computing processes. The proposed framework was evaluated with the unstructured COVID-19 medical chest x-rays and MNIST digit datasets, and the structured BoT-IoT dataset. The performance of the framework is comparable with existing authentication methods and reported a higher accuracy than comparable methods (approximately 90% as compared with the 81% reported by peer methods). The proposed authentication method prevents the exposure of sensitive patient information during medical device authentication and would become the cornerstone of the next generation of medical security with serverless computing. [ABSTRACT FROM AUTHOR]

Cross-modal retrieval can break through the limitations of modalities and carry out information retrieval across data of different modalities to meet the needs of users in obtaining multi-modal correlation retrieval. Cloud computing has the advantages of high efficiency and low cost, but data security hinders its development. While cloud computing offers high efficiency and cost-effectiveness, concerns surrounding data security impede its full potential. Privacy-preserving cross-modal retrieval emerges as a viable solution, catering to users’ demands for efficient retrieval while safeguarding data confidentiality. However, a major challenge still exists in this field: how to bridge the inherent semantic gap within heterogeneous and chaotic information. To address this challenge, this paper proposes dual-branch networks for privacy-preserving cross-modal retrieval in cloud computing. Firstly, a dual-branch feature extraction network of encrypted image-text is constructed, enhancing the extraction of meaningful features from encrypted data. Secondly, a cross-modal alignment method is designed to eliminate the heterogeneous gap between different modalities through the alignment within and between modalities. Finally, to fully exploit the storage and computing advantages of cloud computing, both encrypted data and the cross-modal feature extractor are deployed to the cloud. Leveraging the dynamic update capabilities of cloud-stored encrypted data enables continuous model refinement, enhancing retrieval accuracy while reducing the storage and computational burdens on data owners. Extensive experiments conducted on the publicly available benchmark image-text dataset Wikipedia indicate that, compared to existing methods, our approach achieves improvements of 5.4%, 1%, 1.6%, and 20.1% in the four metrics of image-to-text (i2t), text-to-image (t2i), image-to-all (i2all), and text-to-all (t2all), respectively. [ABSTRACT FROM AUTHOR]

Federated learning, as an emerging framework for distributed machine learning, has received widespread attention. In federated learning, the cloud server and the users cooperatively train a model by sharing gradients rather than local private data. However, the users' private data may still be exposed by the shared gradients. Furthermore, the cloud server may perform incorrect aggregation operations on the gradients sent by users and send a forged or previous aggregated gradient to the users. In this paper, we propose PPFLV, a privacy-preserving federated learning scheme with verifiability. Specifically, to protect the users' privacy, we design an efficient double gradient blinding and encryption method to blind and encrypt the users' local gradients. Furthermore, we propose a novel double gradient verification method that can achieve secure verification while resisting replay attacks in the verification phase. With the proposed verification method, the users only require to perform lightweight operations to verify the correctness of the aggregated encrypted gradients and recover the aggregated gradient from the aggregated encrypted gradients. The experimental results show that PPFLV achieves comparable classification accuracy to the basic federated learning scheme while providing privacy protection and verifiability. Furthermore, PPFLV exhibits lower computation and communication overhead compared to related schemes. [ABSTRACT FROM AUTHOR]

Although deep learning models have shown promising results in solving problems related to image recognition or natural language processing, they do not match how the biological brain works. Some of the differences include the amount of energy consumed, the way neurons communicate, or the way they learn. To close the gap between artificial neural networks and biological ones, researchers proposed the spiking neural network. Layered Spiking Neural P systems (LSN P systems) are networks of spiking neurons used to solve various classification problems. In this paper, we study the LSN P systems in the context of a federated learning client–server architecture over horizontally partitioned data. We analyze the privacy implications of pre-trained LSN P systems through membership inference attacks. We also perform experiments to assess the performance of an LSN P system trained in the federated learning setup. Our findings suggest that LSN P systems demonstrate higher accuracy and faster convergence compared to federated algorithms based on either perceptron or spiking neural networks. [ABSTRACT FROM AUTHOR]

Implementing a cloud-based data warehouse to store sensitive or critical strategic data presents challenges primarily related to the security of the stored information and the exchange of OLAP queries between the cloud server and users. Although encryption is a viable solution for safeguarding outsourced data, applying it to OLAP queries involving multidimensional data, measures, and Multidimensional Expressions (MDX) operations on encrypted data poses difficulties. Existing searchable encryption solutions are inadequate for handling such complex queries, which complicates the use of business intelligence tools that rely on efficient and secure data processing and analysis.This paper proposes a new privacy-preserving cloud data warehouse scheme called SSF-CDW which facilitates a secure and scalable solution for an encrypted cloud data warehouse. Our SSF-CDW improves the OLAP queries accessible only to authorized users who can decrypt the query results with improved query performance compared to traditional OLAP tools. The approach involves utilizing symmetric encryption and Ciphertext Policy Attribute-Based Encryption (CP-ABE) to protect the privacy of the dimension and fact data modeled in Multidimensional OLAP (MOLAP). To support efficient OLAP query execution, we proposed a new data cube retrieval mechanism using a Redis schema which is an in-memory database. This technique dynamically compiles queries by disassembling them down into multiple levels and consolidates the results mapped to the corresponding encrypted data cube. The caching of dimensional and fact data associated with the encrypted cube is also implemented to improve the speed of frequently queried data. Experimental comparisons between our proposed indexed search strategy and other indexing schemes demonstrate that our approach surpasses alternative techniques in terms of search speed for both ad-hoc and repeated OLAP queries, all while preserving the privacy of the query results. [ABSTRACT FROM AUTHOR]

Breast cancer is deadly cancer causing a considerable number of fatalities among women in worldwide. To enhance patient outcomes as well as survival rates, early and accurate detection is crucial. Machine learning techniques, particularly deep learning, have demonstrated impressive success in various image recognition tasks, including breast cancer classification. However, the reliance on large labeled datasets poses challenges in the medical domain due to privacy issues and data silos. This study proposes a novel transfer learning approach integrated into a federated learning framework to solve the limitations of limited labeled data and data privacy in collaborative healthcare settings. For breast cancer classification, the mammography and MRO images were gathered from three different medical centers. Federated learning, an emerging privacy-preserving paradigm, empowers multiple medical institutions to jointly train the global model while maintaining data decentralization. Our proposed methodology capitalizes on the power of pre-trained ResNet, a deep neural network architecture, as a feature extractor. By fine-tuning the higher layers of ResNet using breast cancer datasets from diverse medical centers, we enable the model to learn specialized features relevant to different domains while leveraging the comprehensive image representations acquired from large-scale datasets like ImageNet. To overcome domain shift challenges caused by variations in data distributions across medical centers, we introduce domain adversarial training. The model learns to minimize the domain discrepancy while maximizing classification accuracy, facilitating the acquisition of domain-invariant features. We conducted extensive experiments on diverse breast cancer datasets obtained from multiple medical centers. Comparative analysis was performed to evaluate the proposed approach against traditional standalone training and federated learning without domain adaptation. When compared with traditional models, our proposed model showed a classification accuracy of 98.8% and a computational time of 12.22 s. The results showcase promising enhancements in classification accuracy and model generalization, underscoring the potential of our method in improving breast cancer classification performance while upholding data privacy in a federated healthcare environment. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, clients and users with limited computing resources can outsource their computation-intensive tasks to the Cloud Service Providers (CSPs). However, as the CSPs are commercial in nature and aim to increase their profits, some security challenges are still attached to them. In this paper, we propose an efficient publicly verifiable computation scheme (EPVM) for large-scale matrix multiplication with privacy preservation. Based on the theory of discrete logarithm problem and the techniques of privacy-preserving matrix transformation, our scheme not only protects the privacy of the client's matrices but also significantly reduces the computation overhead on the client end as well as the CSP side. Our detailed security analysis and proofs show that the proposed scheme can achieve the established security requirements. The experimental evaluation also demonstrates that the proposed scheme works efficiently as compared to other existing solutions. [ABSTRACT FROM AUTHOR]

Federated learning (FL) represents a novel privacy‐preserving learning paradigm that offers a practical solution for distributed privacy preservation. Although privacy‐preserving FL based on homomorphic encryption (HE‐PPFL) exhibits resistance to gradient leakage attacks while ensuring the accuracy of aggregation results, its widespread adoption in blockchain privacy preservation is hindered by the reliance on a trusted key generation center and secure transfer channels. Conversely, coverless steganography schemes effectively ensure the covert transmission of sensitive information across insecure channels. However, their incompatibility with HE‐PPFL arises from the lossy extraction process. To address these challenges, we present a decentralized federated learning privacy‐preserving framework based on the Lifted ElGamal threshold decryption cryptosystem. We introduce a reversible steganography method tailored to safeguard gradient privacy. Furthermore, we introduce a lightweight, secure blind aggregation algorithm founded on the Raft protocol, which serves to protect gradient privacy while substantially mitigating computational overhead. Finally, we provide rigorous theoretical proof of the security and correctness of our proposed scheme. Experimental results from four public data sets demonstrate that our proposed scheme achieves a 100% extraction accuracy without the need for lossless methods, while simultaneously reducing the computational cost of ciphertext gradient aggregation by at least three orders of magnitude. The FedSteg framework is publicly accessible at https://github.com/Xumeili/FedSteg. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Knowledge Graph (KG) has attracted more and more companies' attention for its ability to connect different types of data in meaningful ways and support rich data services. However, due to privacy concerns, different companies cannot share their own KGs with each other. Such data isolation problem limits the performance of KG and prevents its further development. Therefore, how to let multiple parties conduct KG-related tasks collaboratively on the basis of privacy protection becomes an important research question to answer. In this paper, to fill this gap, we summarize the open problems for privacy-preserving KG in the data isolation setting and propose possible solutions for them. Specifically, we summarize the open problems in privacy-preserving KG from four aspects, i.e., merging, query, representation, and completion. We present these problems in detail and propose possible technical solutions for them, along with the datasets, evaluation methods, and future research directions. We also provide three privacy-preserving KG application scenarios. [ABSTRACT FROM AUTHOR]

With the rapid development of artificial intelligence and Big Data, the application of artificial intelligence-generated image content (AIGIC) is becoming increasingly widespread in various fields. However, the image data utilized by AIGIC is diverse and often contains sensitive personal information, characterized by heterogeneity and privacy concerns. This leads to prolonged implementation times for image data privacy protection, and a high risk of unauthorized third-party access, resulting in serious privacy breaches and security risks. To address this issue, this paper combines Hierarchical Federated Learning (HFL) with Homomorphic Encryption to first address the encryption and transmission challenges in the image processing pipeline of AIGIC. Building upon this foundation, a novel HFL group collaborative training strategy is designed to further streamline the privacy protection process of AIGIC image data, effectively masking the heterogeneity of raw image data and achieving balanced allocation of computational resources. Additionally, a model compression algorithm based on pruning is introduced to alleviate the data transmission pressure in the image encryption process. Optimization of the homomorphic encryption modulo operations significantly reduces the computational burden, enabling real-time enhancement of image data privacy protection from multiple dimensions including computational and transmission resources. To verify the effectiveness of the proposed mechanism, extensive simulation verification of the lightweight privacy protection process for AIGIC image data was performed, and a comparative analysis of the time complexity of the mechanism was conducted. Experimental results indicate substantial advantages of the proposed algorithm over traditional real-time privacy protection algorithms in AIGIC. [ABSTRACT FROM AUTHOR]

This paper addresses the privacy concerns inherent in semantic communication within the Internet of Things (IoT) and proposes a Secure Semantic Communication Framework (SSCF) to ascertain confidentiality and communication accuracy without compromising semantic integrity. The proposed framework uses the Advanced Encryption Standard (AES) for encryption to address privacy breaches in semantic communication. Additionally, it introduces a novel approach employing Deep Q-Networks (DQN) for adversarial training to maintain semantic communication accuracy in both unencrypted and encrypted modes. SSCF combines universality and confidentiality, ensuring secure and efficient semantic communication. Experimental evaluations showed that SSCF, with its adversarial encryption learning scheme, effectively ensures communication accuracy and privacy. Regardless of encryption status, SSCF significantly hinders attackers from restoring original semantic data from intercepted messages. The integration of heuristic algorithms enhances performance and security. The proposed framework is based on a shared database for training network modules. The originality of the proposed approach lies in the introduction of a DQN-based adversarial training technique to balance confidentiality and semantic communication accuracy, address key privacy concerns, and enhance the security and reliability of IoT communication systems. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) faces security challenges in maintaining privacy due to the presence of open networks and diverse services. Ensuring privacy is essential in transportation networks to provide users with a long-lasting driving, navigation, and communication experience. In this paper, the proposed Privacy Recommending Data Processing Model (PRDPM) is deployed to handle the huge amount of data accumulated in this field. The proposed model adopts data processing techniques that are dependent on user demand and are influenced by either neighboring entities or service providers. The various application requirements are analyzed to minimize the potential privacy consequences. The data from various intervals are utilized to validate the parameters in the operational plane. Thus, data balancing is performed using plane differentiation to prevent privacy leaks in either of the vehicular services. This is useful for neighbors and infrastructures across various applications/users. [ABSTRACT FROM AUTHOR]

This study builds on our previous systematic literature review (SLR) that assessed the applications and performance of zk-SNARK, zk-STARK, and Bulletproof non-interactive zero-knowledge proof (NIZKP) protocols. To address the identified research gaps, we designed and implemented a benchmark comparing these three protocols using a dynamic minimized multiplicative complexity (MiMC) hash application. We evaluated performance across four general-purpose programming libraries and two programming languages. Our results show that zk-SNARK produced the smallest proofs, while zk-STARK generated the largest. In terms of proof generation and verification times, zk-STARK was the fastest, and Bulletproof was the slowest. Interestingly, zk-SNARK proofs verified marginally faster than zk-STARK, contrary to other findings. These insights enhance our understanding of the functionality, security, and performance of NIZKP protocols, providing valuable guidance for selecting the most suitable protocol for specific applications. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) represents a significant paradigm shift in network architecture, separating network logic from the underlying forwarding devices to enhance flexibility and centralize deployment. Concurrently, the Internet of Things (IoT) connects numerous devices to the Internet, enabling autonomous interactions with minimal human intervention. However, implementing and managing an SDN-IoT system is inherently complex, particularly for those with limited resources, as the dynamic and distributed nature of IoT infrastructures creates security and privacy challenges during SDN integration. The findings of this study underscore the primary security and privacy challenges across application, control, and data planes. A comprehensive review evaluates the root causes of these challenges and the defense techniques employed in prior works to establish sufficient secrecy and privacy protection. Recent investigations have explored cutting-edge methods, such as leveraging blockchain for transaction recording to enhance security and privacy, along with applying machine learning and deep learning approaches to identify and mitigate the impacts of Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Moreover, the analysis indicates that encryption and hashing techniques are prevalent in the data plane, whereas access control and certificate authorization are prominently considered in the control plane, and authentication is commonly employed within the application plane. Additionally, this paper outlines future directions, offering insights into potential strategies and technological advancements aimed at fostering a more secure and privacy-conscious SDN-based IoT ecosystem. [ABSTRACT FROM AUTHOR]

The proliferation of intelligent, connected Internet of Things (IoT) devices facilitates data collection. However, task workers may be reluctant to participate in data collection due to privacy concerns, and task requesters may be concerned about the validity of the collected data. Hence, it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing (SC) data collection tasks with IoT. To this end, this paper proposes a privacy-preserving data reliability evaluation for SC in IoT, named PARE. First, we design a data uploading format using blockchain and Paillier homomorphic cryptosystem, providing unchangeable and traceable data while overcoming privacy concerns. Secondly, based on the uploaded data, we propose a method to determine the approximate correct value region without knowing the exact value. Finally, we offer a data filtering mechanism based on the Paillier cryptosystem using this value region. The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection. [ABSTRACT FROM AUTHOR]

Preserving the privacy of the ever-increasing multimedia data on the cloud while providing accurate and fast retrieval services has become a hot topic in information security. However, existing relevant schemes still have significant room for improvement in accuracy and speed. Therefore, this paper proposes a privacy-preserving image–text retrieval scheme called PITR. To enhance model performance with minimal parameter training, we freeze all parameters of a multimodal pre-trained model and incorporate trainable modules along with either a general adapter or a specialized adapter, which are used to enhance the model's ability to perform zero-shot image classification and cross-modal retrieval in general or specialized datasets, respectively. To preserve the privacy of outsourced data on the cloud and the privacy of the user's retrieval process, we employ asymmetric scalar-product-preserving encryption technology suitable for inner product calculation, and we employ distributed index storage technology and construct a two-level security model. We construct a hierarchical index structure to speed up query matching among massive high-dimensional index vectors. Experimental results demonstrate that our scheme can provide users with secure, accurate, fast cross-modal retrieval service while preserving data privacy. [ABSTRACT FROM AUTHOR]

With the popularity of the media cloud computing industry, individuals and organizations outsource image computation and storage to the media cloud server to reduce the storage burden. Media images usually contain a large amount of private information. To prevent disclosure of privacy of the image owners, media images are encrypted before uploading to the server. However, this operation will greatly limit the utilization of the image for the user, such as content-based image retrieval. We propose an efficient similarity query algorithm with access control based on Bkd-tree in this paper, in which a searchable encryption scheme is designed for similarity image retrieval, and the encrypted image is used to extract image features by a pre-trained CNN model. The Bkd-tree is utilized to generate an index tree for the image features to speed up retrieval and make it faster than linear indexing. Finally, the security performances of the proposed scheme is analyzed and the performance of this scheme is evaluated by experiments. The results show that the security of the image content and image features can be ensured, and it has a shorter retrieval time and higher retrieval efficiency. [ABSTRACT FROM AUTHOR]

The rapid development of mobile communication technologies and Internet of Things (IoT) devices has introduced new challenges for multi-access edge computing (MEC). A key issue is how to efficiently manage MEC resources and determine the optimal offloading strategy between edge servers and user devices, while also protecting user privacy and thereby improving the Quality of Service (QoS). To address this issue, this paper investigates a privacy-preserving computation offloading scheme, designed to maximize QoS by comprehensively considering privacy protection, delay, energy consumption, and the task discard rate of user devices. We first formalize the privacy issue by introducing the concept of privacy entropy. Then, based on quantified indicators, a multi-objective optimization problem is established. To find an optimal solution to this problem, this paper proposes a computation offloading algorithm based on the Twin delayed deep deterministic policy gradient (TD3-SN-PER), which integrates clipped double-Q learning, prioritized experience replay, and state normalization techniques. Finally, the proposed method is evaluated through simulation analysis. The experimental results demonstrate that our approach can effectively balance multiple performance metrics to achieve optimal QoS. [ABSTRACT FROM AUTHOR]

With the widespread use of the internet and advancements in computing and data storage technologies, large amounts of data can be collected and analyzed to explore hidden knowledge and patterns in various areas. Among different data types, images have become an increasingly important information carrier, especially in computer vision research. However, the image data often contains valuable sensitive information of users, such as personal identifiers and biometric information. During the transmission process, once adversaries obtain the image data, it may lead to severe consequences. To solve the image data leakage problem, a lot of image privacy-preserving schemes are proposed. Nevertheless, in most existing schemes, the utility of an image will also be broken while ensuring data privacy. Meanwhile, how to balance privacy and utility is always a nerve-wracking challenge in image privacy protection. In this paper, we focus on designing a privacy-preserving scheme that protects the visual content of an image while retaining its availability for convolutional neural networks (CNN) training and prediction. Specifically, we first use the edge detection technique to discover original image features. Then, we carefully design a noise generation method in which the generated noises can guarantee distance-based indistinguishability while minimally affecting image features. Therefore, with the proposed scheme, the visual content of an image can be protected, and it can still be used for CNN training and prediction. Moreover, we empirically evaluate our proposed scheme with various evaluation criteria. The results demonstrate that the visual content of an image is indeed protected while the accuracy of the trained CNN model is available. [ABSTRACT FROM AUTHOR]

With the increasing prevalence of cloud computing platforms, ensuring data privacy during the cloud-based image-related services such as classification has become crucial. In this study, we propose a novel privacy-preserving image classification scheme that enables the direct application of classifiers trained in the plaintext domain to classify encrypted images without the need of retraining a dedicated classifier. Moreover, encrypted images can be decrypted back into their original form with high fidelity (recoverable) using a secret key. Specifically, our proposed scheme involves utilizing a feature extractor and an encoder to mask the plaintext image through a newly designed Noise-like Adversarial Example (NAE). Such an NAE not only introduces a noise-like visual appearance to the encrypted image but also compels the target classifier to predict the ciphertext as the same label as the original plaintext image. At the decoding phase, we adopt a Symmetric Residual Learning (SRL) framework for restoring the plaintext image with minimal degradation. Extensive experiments demonstrate that (1) the classification accuracy of the classifier trained in the plaintext domain remains the same in both the ciphertext and plaintext domains; (2) the encrypted images can be recovered into their original form with an average PSNR of up to 51+ dB for the SVHN dataset and 48+ dB for the VGGFace2 dataset; (3) our system exhibits satisfactory generalization capability on the encryption, decryption, and classification tasks across datasets that are different from the training one; and (4) a high-level of security is achieved against three potential threat models. The code is available at https://github.com/csjunjun/RIC.git. [ABSTRACT FROM AUTHOR]

FedLens is the federated learning model based on viewing world tourist spots virtually in a privacy-preserving manner. We define Virtual Tourism as enjoying the natural beauty, other related activities online, using AR/VR/MR technology-based 'virtual eye', to interact actively with nature and people at tourist spots. Federated learning-based mobile crowdsensing is an emerging collaborative distributed learning paradigm for privacy-preserving, energy-efficient, and scalable networks. Edge intelligent mobile crowdsensing uses geotagged tourist attractions. The purpose of this study is to explore the geo-statistics of tourist areas. The proposed 'FedLens' brings tourists closer to the interests using augmented reality through the virtual guide. ArcGIS software maps a tourist area. 5G mobile crowdsensing helps to explore unknown tourist spots in real time. 'FedLens' provides a privacy-preserving incentive mechanism to encourage reliable contributors to get better Quality of Information. The average global data aggregation time is approximately 12%. The contributor's collection time is 88% of the total processing time. The contributors use multifaceted intelligent federated computing to provide detailed geospatial information and promote sustainable ecotourism. Augmented reality-based virtual tourism ecosystem development is the ultimate goal of this work to attract more virtual tourists for a sustainable environment. Future physical tour-planning recommendation systems are incorporated in the proposed model. [ABSTRACT FROM AUTHOR]

Copyright of Application Research of Computers / Jisuanji Yingyong Yanjiu is the property of Application Research of Computers Edition and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Federated Learning (FL), as an emergent paradigm in privacy-preserving machine learning, has garnered significant interest from scholars and engineers across both academic and industrial spheres. Despite its innovative approach to model training across distributed networks, FL has its vulnerabilities; the centralized server-client architecture introduces risks of single-point failures. Moreover, the integrity of the global model--a cornerstone of FL--is susceptible to compromise through poisoning attacks by malicious actors. Such attacks and the potential for privacy leakage via inference starkly undermine FL's foundational privacy and security goals. For these reasons, some participants unwilling use their private data to train a model, which is a bottleneck in the development and industrialization of federated learning. Blockchain technology, characterized by its decentralized ledger system, offers a compelling solution to these issues. It inherently prevents single-point failures and, through its incentive mechanisms, motivates participants to contribute computing power. Thus, blockchain-based FL (BCFL) emerges as a natural progression to address FL's challenges. This study begins with concise introductions to federated learning and blockchain technologies, followed by a formal analysis of the specific problems that FL encounters. It discusses the challenges of combining the two technologies and presents an overview of the latest cryptographic solutions that prevent privacy leakage during communication and incentives in BCFL. In addition, this research examines the use of BCFL in various fields, such as the Internet of Things and the Internet of Vehicles. Finally, it assesses the effectiveness of these solutions. [ABSTRACT FROM AUTHOR]

The utilization of a biometric authentication system (BAS) for reliable automatic human recognition has increased exponentially in recent years over traditional authentication systems. Since the biometric traits are irrevocable, two important issues such as security and privacy still need to be addressed in BAS. Researchers explore homomorphic encryption (HE) to propose several privacy-preserving BAS. However, the correctness of the evaluated results computed by the cloud server on the protected templates is still an open research challenge. These methods are able to conserve the privacy of biometric templates but unable to check the correctness of computed result results in false reject or accept. To overcome this issue, we suggest a reliable and privacy-preserving verifiable multi-instance iris verification system using Paillier HE and one-digit checksum (PVMIAPO). Modified local random projection is implemented on the fused iris template to produce the reduced template. Later, Paillier HE is applied on the reduced template to create the protected template. The result returned by the third party server is verified using the one-digit checksum. The efficiency of PVMIAPO is verified by experimenting with it on SDUMLA-HMT, IITD, and CASIA-V3-Interval iris databases. PVMIAPO gratifies the irreversibility, diversity, and revocability properties. PVMIAPO also obtains fair performance in contrast to the existing methods. [ABSTRACT FROM AUTHOR]

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

The amount of data generated owing to the rapid development of the Smart Internet of Things is increasing exponentially. Traditional machine learning can no longer meet the requirements for training complex models with large amounts of data. Federated learning, as a new paradigm for training statistical models in distributed edge networks, alleviates integration and training problems in the context of massive and heterogeneous data and security protection for private data. Edge computing processes data at the edge layers of data sources to ensure low-data-delay processing; it provides high-bandwidth communication and a stable network environment, and relieves the pressure of processing massive data using a single node in the cloud center. A combination of edge computing and federated learning can further optimize computing, communication, and data security for the edge-Internet of Things. This review investigated the development status of federated learning and expounded on its basic principles. Then, in view of the security attacks and privacy leakage problems of federated learning in the edge Internet of things, relevant work was investigated from cryptographic technologies (such as secure multi-party computation, homomorphic encryption and secret sharing), perturbation schemes (such as differential privacy), adversarial training and other privacy security protection measures. Finally, challenges and future research directions for the integration of edge computing and federated learning are discussed. [ABSTRACT FROM AUTHOR]