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As a decentralized network infrastructure, the data sent to the blockchain are public and temper-evident. The cover of massive normal transactions in a blockchain network is ideal for constructing a stable and reliable covert channel to achieve one-to-many group covert communication. Existing blockchain-based covert communication schemes face challenges in balancing concealment, embedding rate and filtering efficiency, making them unsuitable for direct extension to group scenarios. Adopting a key-leakage scheme can increase the channel capacity while maintaining high concealment from external adversaries. However, it will also expose more knowledge to the receiver. A malicious receiver has the ability to steal a sender's identity or replay historical transactions to control the entire channel. In this paper, we define the capabilities of malicious receivers in blockchain-based group covert communication scenarios and propose a group covert communication scheme resistant to transaction forgery attacks. Theoretical analysis and experiments prove that our covert transactions do not have any transaction correlativity, ensuring the unique authenticity of the sender's identity while maintaining supreme concealment compared with the existing schemes. The precision and recall of machine learning detection results can reach 0.57–0.62 (0.5 is the ideal value). [ABSTRACT FROM AUTHOR]

Bitcoin was launched over a decade ago and has made an increasing impact on the world's financial order, which has attracted the attention of researchers all over the world. The Bitcoin system runs on a dynamic P2P network, containing tens of thousands of nodes, including reachable nodes and unreachable nodes. In this article, a detection system, BNS (Bitcoin Network Sniffer), which could collect as many Bitcoin nodes as possible is proposed. For reachable nodes, the authors designed an algorithm, BRF (Bitcoin Reachable-Nodes Finding), based on node activity evaluation which reduces the nodes to be detected and greatly shortens the detection time. For unreachable nodes, the authors trained a decision tree model, BUF (Bitcoin Unreachable-Nodes Finding), to identify unreachable nodes based on attribute features from a large number of node addresses. Experiments showed that BNS discovered an average of 1093 more reachable nodes (6.4%) and 662 more unreachable nodes (2.3%) than the well-known website "Bitnodes" per day. It showed better performance in total nodes and efficiency. Based on the experimental results, the authors analyzed the real network size, node "churn", and geographical distribution. [ABSTRACT FROM AUTHOR]

Reporting helps to combat illegal activities and deters lawbreakers and potential lawbreakers. From ancient times to the present, public authorities have usually rewarded effective reporting information to build harmonious societies. In this process, protecting the privacy of the whistleblower is a very important issue. Existing blockchain-based anonymous reporting solutions help solve the problem of insufficient anonymity in traditional reporting solutions, but they do not address the issue of hiding the reporting behavior. The disclosure of reporting behavior may alert offenders in advance and negatively impact case handling. This paper proposes an anonymous and covert reporting scheme and rewarding mechanism based on blockchain, which realizes the covertness of the reporting behavior while protecting the privacy of the whistleblower. The proposed scheme uses ring signature and derived address technology to ensure anonymity and achieves covertness by embedding information in the ring signature based on the idea of covert communication. Theoretical analysis proves that the proposed scheme has covertness, anonymity, and unforgeability properties. Experiments show that the proposed scheme takes only 0.08 s to upload data and 0.07 s to verify while achieving covertness. [ABSTRACT FROM AUTHOR]

Based on the improved BP neural network, this paper establishes an adaptive online controlling model and adopts the model to optimize the controlling accuracies in discrete nonlinear dynamic systems and inverted pendulum systems. To avoid the local minimum problem of the BP neural network's objective function in the training process, this paper proposes a neural network training method based on the quasi-Newton method (BFGS) optimization algorithm. Compared with other control methods, the neural network-based inverted pendulum control method proposed in this paper has higher control accuracy. Through the control simulation of its power system uses a discrete control method and the control of the inverted pendulum model system, this paper verifies the validity and good control has significantly improved the control method. [ABSTRACT FROM AUTHOR]

Crowdsourcing-based traffic monitoring plays an important role in advanced traffic management systems due to its high accuracy and low costs, but it may expose drivers real identities and sensitive locations that results in the privacy leakage of drivers. In this paper, we propose a crowdsourcing-based traffic monitoring scheme that enables a transportation management center (TMC) to achieve traffic flow statistics at road intersections in an efficient, verifiable, and privacy-preserving manner. Specifically, by integrating a homomorphic encryption primitive and a super-increasing sequence, traffic flow can be flexibly structured and encrypted by drivers, i.e., each drivers travel direction at T-junctions or crossroads is protected. As a middle-ware between drivers and TMC, roadside units (RSUs) are introduced to aggregate and further perturb the aggregated encrypted traffic flow based on a differential privacy mechanism. In this way, TMC is capable of acquiring the traffic flow statistics by decrypting the perturbed encrypted traffic flow, without disclosing each individual drivers traffic information. In addition, based on a lightweight commitment proof, the correctness of the encrypted drivers data can be guaranteed, i.e., a selfish driver cannot arbitrarily manipulate his data to poison the aggregated traffic flow. Finally, security analysis demonstrates that the proposed scheme satisfies all desirable security properties, including confidentiality, verifiability, unlinkability, and traceability. Extensive simulations are also conducted to show that the proposed scheme is efficient in terms of low computation and communication costs. [ABSTRACT FROM AUTHOR]

Blockchain technology is an immutable database that cannot be censored, tampered with, or erased, which can increase credibility and reduce interaction costs. Therefore, blockchain technology is expected to become the next generation Internet infrastructure, with broad application prospects in many industries, such as medicine, energy, and finance. The open network and publicly stored data in a blockchain system, however, raise serious risks of data theft and user privacy leakage, which has become a core issue that restricts the blockchain technology from becoming practical. This article uses kleptography technology to design a blockchain covert data transmission scheme to achieve high-concealment and high-performance data transmission under open network conditions. Experiments conducted in the Bitcoin network prove that the covert data transmission mechanism of this article has high compatibility and high performance, and can be practically applied to many popular blockchain systems. [ABSTRACT FROM AUTHOR]

Driver identification faces various challenges in real-time applications. These challenges include high dimensional input data, moderate accuracy, scalability issues and need for custom-built in-vehicle sensors. The conventional biometric solutions are either less accurate, unscalable, or costly for practical application. In those solutions, high accuracy comes at the cost of the preservation of privacy. Similarly, scalability and cost-effectiveness are inversely proportional to one another. This paper proposes a driver identification scheme to pare these challenges. The scheme uses data from the global positioning system (GPS) to learn an individual's driving pattern, which is commonly deployed in in-car navigation systems and can also be found in general-purpose hand-held devices in the prevailing market. The proposed scheme is innovative in terms of providing a single solution for the existing challenges. It is more practical and scalable with large numbers of the drivers also being cost-effective and accurate, which makes it applicable in real-time applications with the least overhead costs for different resources. The consequent analysis and empirical results show that the scheme could identify drivers with significant accuracy given only GPS data. To assess the scheme in-depth, we perform experiments both on the collected data and the real-world open datasets. The average accuracy approximates above 96% for up to 25 drivers. [ABSTRACT FROM AUTHOR]

Fog computing has enabled the extension of cloud computing functions to the network edge by assisting the cloud and users in terms of communication, computation, and storage with a widely spread deployment of edge devices or fog nodes, as they become more powerful in capacities and functionalities. Smart grid is becoming the next-generation power grid and will automate reliable electricity distribution for millions of customers. However, the data transmitted in smart grid communications has reached the terabyte level and the mishandling of smart meter readings may lead to customers' privacy leakage. In this article, we present an architecture of fog-based smart grid, discuss its applications, security and privacy challenges, and introduce several state-of-the-art works. We also propose a privacy-preserving authentication and data aggregation scheme for fog-based smart grid. Specifically, we use short randomizable signature and blind signature to provide anonymous authentication in a conditional way, and then we utilize fog nodes to solve billing problems after anonymous authentication. Also, we aggregate the smart meter readings by the homomorphic Paillier cryptosystem. Finally, we aim to attract more attention to this emerging topic by giving some interesting future directions. [ABSTRACT FROM AUTHOR]

As an integral part of V2G networks, EVs receive electricity from not only the grid but also other EVs and may frequently feed the power back to the grid. Payment records in V2G networks are useful for extracting user behaviors and facilitating decision-making for optimized power supply, scheduling, pricing, and consumption. Sharing payment and user information, however, raises serious privacy concerns in addition to the existing challenge of secure and reliable transaction processing. In this article, we propose a blockchain-based privacy preserving payment mechanism for V2G networks, which enables data sharing while securing sensitive user information. The mechanism introduces a registration and data maintenance process that is based on a blockchain technique, which ensures the anonymity of user payment data while enabling payment auditing by privileged users. Our design is implemented based on Hyperledger to carefully evaluate its feasibility and effectiveness. [ABSTRACT FROM AUTHOR]

Zhu, H. and Zhu, L., 2018. The localization method for ship network nodes based on high performance matching of multi-pattern string TMWM. In: Liu, Z.L. and Mi, C. (eds.), Advances in Sustainable Port and Ocean Engineering. Journal of Coastal Research, Special Issue No. 83, pp. 776–780. Coconut Creek (Florida), ISSN 0749-0208. The pattern string matching used in the current method is single pattern matching. The matching efficiency and accuracy are low, and a large number of CPU resources are used, which reduces the efficiency and accuracy of the localization of the ship network nodes. In this paper, a high performance matching method based on multi-pattern string TMWM is proposed to preprocess the ship network nodes and make multi-pattern matching, until all the strings are terminated. Application of multi-pattern matching algorithm improves the matching efficiency, and will not take up too much CPU resources, which can increase the efficiency and accuracy of localization. The use of mobile beacon randomly walks in the network nodes distribution area of ship, periodically broadcasts their localization information, and makes localization of network nodes on any ships. Experimental results show that the proposed method improves the efficiency and accuracy of pattern string matching, and can achieve high precision and high efficiency localization. [ABSTRACT FROM AUTHOR]

Bitcoin is a decentralized digital cryptocurrency. Its network is a Peer-to-peer(P2P) network consisting of distributed nodes. Some of these nodes are always online and in this article are called Bitcoin backbone nodes. They have a significant impact on the stability and security of the Bitcoin network, so it is meaningful to analyze and discuss them. In this paper, we first continuously collect information about Bitcoin nodes from July 2021 through June 2022 (which is the longest duration of data collection to date). In total, we collect information on 127,613 Bitcoin nodes. At the same time, we conclude that the fluctuation of Bitcoin nodes is directly related to the fluctuation of onion network nodes. Further, we filtered 2694 Bitcoin backbone nodes based on our algorithm. By analyzing the backbone nodes' attributes such as geographic distribution, client version, operator, node function, and abnormal port number, it is demonstrated that these nodes are centralized and play an important role in the Bitcoin network. Based on this, three unsupervised machine learning algorithms are selected to cluster multiple attributes of backbone nodes in a more scientific way. In this paper, the whole process from data collection to cluster analysis is completed and the best results are obtained by comparison. The experiments proved the existence of centralization of Bitcoin backbone nodes and obtained the number of nodes within each cluster. Finally, cluster nodes are de-anonymized based on the optimal results. Through our experiments, we obtain organizational information about the deployers of 103 nodes, linking the Bitcoin backbone nodes to the real world, thus accurately demonstrating the existence of Bitcoin centrality. [ABSTRACT FROM AUTHOR]

CVCC has garnered significant attention in recent years as a special cloud computing platform capable of broadening network service provisioning in mobile computing. Vehicular crowdsensing is a prime candidate for CVCC applications as connected vehicles can provide tremendous sensing, computing, and storage resources. Truthfulness of sensing data is very important, as malicious vehicles may create inaccuracy in sensing results. In this work, we propose RTSense, which enables trust-based crowdsensing services in CVCC. The architecture divides the system into control and data planes, where the trust authority and service providers sit in the control plane, and vehicles and fogs exist in the data plane. We provide solutions for anonymous vehicle authentication, interactive filtering truth discovery, and trust management for reliable crowdsensing. The experimental analysis shows that RTSense can effectively segregate malicious and trustworthy vehicles. We also identify interesting future directions along with possible solutions. [ABSTRACT FROM AUTHOR]

Since the emergence of smart meters, they have played an important role in providing energy- efficient and economical service in smart grids, and they have been extensively deployed. Meanwhile, the incessant energy consumption data collected from smart meters also poses a serious threat to customer privacy. In this article, we present a systematic study of the state-of-the-art privacy- preserving schemes for smart meters. First, we introduce and formulate the technical challenges existing in practice. After that, we categorize and review several representative privacy-preserving schemes according to their respective design goals. Finally, we introduce three common quantitative evaluation metrics for privacy loss, and analyze the privacy leakage of the state-of-the-art schemes. [ABSTRACT FROM AUTHOR]

Selecting appropriate services for task execution in workflows should not only consider budget and deadline constraints, but also ensure the best probability that workflow will succeed and minimize the potential loss in case of exceptions. This requirement is more critical for data-intensive applications in grids or clouds since any failure is costly. Therefore, we design a fine-grained risk evaluation model customized for workflows to precisely compute the cost of failure for selected services. In comparison with current course-grained model, ours takes the relation of task dependency into consideration and assigns higher impact factor to tasks at the end. Thereafter, we design the utility function with the model and apply a genetic algorithm to find the optimized service allocations, thereby maximizing the robustness of the workflow while minimizing the possible risk of failure. Experiments and analysis show that the application of customized risk evaluation model into service selection can generally improve the successful probability of a workflow while reducing its exposure to the risk. [ABSTRACT FROM AUTHOR]

Wireless sensor networks consist of a great number of sensor nodes with strictly limited computation capability, storage, communication resources, and battery power. Because they are deployed in remote and hostile environments and hence are vulnerable to physical attacks, sensor networks face many practical challenges. Data confidentiality, data integrity, source authentication, and availability are all major security concerns. In this paper, we focus on the very problem of preserving data integrity and propose an Efficient Integrity-Preserving Data Aggregation Protocol (EIPDAP) to guarantee the integrity of aggregation result through aggregation in sensor networks. In EIPDAP, base station can immediately verify the integrity of aggregation result after receiving the aggregation result and corresponding authentication information. However, to check integrity, most existing protocols need an additional phase which will consume a lot of energy and cause network delay. Compared with other related schemes, EIPDAP reduces the communication overhead per node to O (Δ) , where Δ is the degree of the aggregation tree for the network. To the best of our knowledge, EIPDAP has the most optimal upper bound on solving the integrity-preserving data aggregation problem. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are composed of sensor nodes with limited energy which is difficult to replenish. Data aggregation is considered to help reduce communication overhead with in-network processing, thus minimizing energy consumption and maximizing network lifetime. Meanwhile, it comes with challenges for data confidentiality protection. Many existing confidentiality preserving aggregation protocols have to transfer list of sensors' ID for base station to explicitly tell which sensor nodes have actually provided measurement. However, forwarding a large number of node IDs brings overwhelming extra communication overhead. In this paper, we propose provably secure aggregation scheme perturbation-based efficient confidentiality preserving protocol (PEC2P) that allows efficient aggregation of perturbed data without transferring any ID information. In general, environmental data is confined to a certain range; hence, we utilize this feature and design an algorithm to help powerful base station retrieve the ID of reporting nodes. We analyze the accuracy of PEC2P and conclude that base station can retrieve the sum of environmental data with an overwhelming probability. We also prove that PEC2P is CPA secure by security reduction. Experiment results demonstrate that PEC2P significantly reduces node congestion (especially for the root node) during aggregation process in comparison with the existing protocols. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a stochastic fuzzy time-varying minimum regret path problem (SFTMRP), which combines the characteristics of the min–max regret path and maximum probability path as a variant of the stochastic fuzzy time-varying shortest path problem, and its purpose is to find a path with the minimum regret degree in a given stochastic fuzzy time-varying network. To address this problem, we propose a random fuzzy delay neural network (RFDNN) based on novel random fuzzy delay neurons and without any training requirements. The random fuzzy delay neuron consists of six layers: an input layer, receiving layer, status layer, generation layer, sending layer, and output layer. Among them, the input and output layers are the ports of communication between neurons, and the receiving layer, status layer, generate layer, and sending layer are the information processing units of neurons. The information exchange between neurons is characterized by two kinds of signals: the shortest path signal and the maximum probability solution signal. The theoretical analysis of the proposed algorithm is carried out with respect to time-complexity and correctness. The numerical example and experimental results on 25 randomly generated stochastic fuzzy time-varying road networks with different numbers of 1000–5000 nodes show that the performance of the proposed algorithm is significantly better than that of existing algorithms. [ABSTRACT FROM AUTHOR]

Vehicles share information via Vehicular Ad-hoc Network (VANET) that is considered the foundation of the intelligent transportation system. Access control from functional encryption (FE) allows secure sharing over encrypted data with both utilization and privacy. However, low-powerful vehicles and the untrusted server incurs that FE-based access control mechanisms are less practical with client-side inefficiency and access unreliability. In this work, we put forth ORAC, an Optimized encrypted Access Control system with partial privacy and Reliability. ORAC is built on top of functional encryption that guarantees data partial privacy and data utilization in VANET, and separates the data access into the offline phase and the online phase, such that most of decryption computation could be done in the offline phase and vehicles could execute the real decryption efficiently. By embedding blockchain transactions into identity records and data storage, ORAC protects data accessed by vehicles from tampering and thereby manages data transfers with high reliability. It can be instantiated directly from pairing-based assumptions and features only the decryption complexity of two exponentiation and two multiplication operations. We give a formal security proof of the instantiation in the semantic security model. We also prototype our scheme by implementing ORAC over Ethereum with two types of smart contracts and the evaluation can demonstrate its efficiency optimization. [ABSTRACT FROM AUTHOR]

With the development of artificial intelligence (AI), deep learning is widely used in various industries. At CRYPTO 2019, researchers used deep learning to analyze the block cipher for the first time and constructed a differential neural network distinguisher to meet a certain accuracy. In this paper, a mixture differential neural network distinguisher using ResNet is proposed to further improve the accuracy by exploring the mixture differential properties. Experiments are conducted on SIMON32/64, and the accuracy of the 8-round mixture differential neural network distinguisher is improved from 74.7% to 92.3%, compared with that of the previous differential neural network distinguisher. The prediction accuracy of the differential neural network distinguisher is susceptible to the choice of the specified input differentials, whereas the mixture differential neural network distinguisher is less affected by the input difference and has greater robustness. Furthermore, by combining the probabilistic expansion of rounds and the neutral bit, the obtained mixture differential neural network distinguisher is extended to 11 rounds, which can realize the 12-round actual key recovery attack on SIMON32/64. With an appropriate increase in the time complexity and data complexity, the key recovery accuracy of the mixture differential neural network distinguisher can be improved to 55% as compared to 52% of the differential neural network distinguisher. The mixture differential neural network distinguisher proposed in this paper can also be applied to other lightweight block ciphers. [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]

The fast development of vehicle positioning technologies has paved the way for in-vehicle recommendation systems. Successive Point-of-Interest (POI) recommendation, one of the most common forms of in-vehicle recommendation, can help users choose places where they might visit in the future. Generally, to recommend POIs for users, raw check-in data are collected from in-vehicle devices, which raises users’ privacy concerns. To address this problem, we propose a successive POI recommendation framework with local differential privacy. The proposed framework exploits three types of influence factors for recommendation, including geographical influence, temporal influence, and transition pattern between two POIs. To protect users’ check-in data, we propose a distance-weighted location perturbation mechanism that realizes local differential privacy. Considering that different users may have different privacy requirements, we enhance this perturbation mechanism to provide personalized privacy protection. We use the EM reconstruction method to correct the error caused by perturbation on the server side. To improve the accuracy, we design an individual-weighted EM reconstruction method. Simulation results demonstrate that the proposed methods can provide strong privacy protection for users without hurting the recommendation accuracy too much. [ABSTRACT FROM AUTHOR]

The explosive number of vehicles has given rise to a series of traffic problems, such as traffic congestion, road safety, and fuel waste. Collecting vehicles' speed information is an effective way to monitor the traffic conditions and avoid vehicles' congestion, however it may threaten vehicles' location and trajectory privacy. Motivated by the fact that traffic monitoring does not need to know each individual vehicle's speed and the average speed would be sufficient, we propose a privacy-preserving traffic monitoring (PPTM) scheme to aggregate vehicles' speeds at different locations. In PPTM, the roadside unit (RSU) collects vehicles' speed information at multiple road segments, and further cooperates with a service provider to calculate the average speed information for every road segment. To preserve vehicles' privacy, both homomorphic Paillier cryptosystem and super-increasing sequence are adopted. A comprehensive security analysis indicates that the proposed PPTM can preserve vehicles' identities, speeds, locations, and trajectories privacy from being disclosed. In addition, extensive simulations are conducted to validate the effectiveness and efficiency of the proposed PPTM scheme. [ABSTRACT FROM AUTHOR]

Medical care has become an indispensable part of people’s lives, with a dramatic increase in the volume of medical data (e.g., diagnosis certificates and medical records). Medical data, however, is easily stolen, tampered with, or even completely deleted. If the above occurs, medical data cannot be recorded or retrieved in a reliable manner, resulting in delay treatment progress, even endanger the patient’s life. In this paper, we propose a novel blockchain-based data preservation system (DPS) for medical data. To provide a reliable storage solution to ensure the primitiveness and verifiability of stored data while preserving privacy for users, we leverage the blockchain framework. With the proposed DPS, users can preserve important data in perpetuity, and the originality of the data can be verified if tampering is suspected. In addition, we use prudent data storage strategies and a variety of cryptographic algorithms to guarantee user privacy; e.g., an adversary is unable to read the plain text even if the data are stolen. We implement a prototype of the DPS based on the real world blockchain-based platform Ethereum. Performance evaluation results demonstrate the effectiveness and efficiency of the proposed system. [ABSTRACT FROM AUTHOR]

Combining the Internet-of-Things (IoT) technology with cloud computing is a significant alternative for powering the utilization of computing resources in a connected environment. A grand challenge in communications is raised by the emergence of big data, due to the large-sized data transmissions and frequent data exchanges. Applying fog computing is considered an option for resolving the communication challenge. However, a high extent of available heterogeneous computing attached to fog computing servers leads to a restriction of the resource management. This Article addresses the resource management issue by proposing a novel approach - named Energy-aware Fog Resource Optimization (EFRO) model- to optimizing the utilization of connected devices in fog computing. We develop a heuristic algorithm minimizing both energy cost and time consumption in a holistic way. A salient feature of EFRO lies in the integration of the standardization and smart shift operations fueled by a hill-climbing mechanism to produce near-optimal resource allocation solutions. Experimental results demonstrate that our EFRO is adroit at making near-optimal decisions in managing resources in fog computing environments. In particular, EFRO boosts the energy efficiency of the existing MESF and RR schemes by 54.83 and 71.28 percent, respectively. EFRO shortens DECM’s allocation-generation time by up to a factor of 507. [ABSTRACT FROM AUTHOR]

The advancements and adoption of cloud-assisted ehealthcare systems have enabled the storage of massive electronic medical records (EMRs) in the cloud for efficient and easy access. A direct benefit of EMRs is the ability of patients to search for EMRs that are similar to their own in the cloud for use as references. These similar EMRs can help a patient find appropriate medical services quickly. However, for large-scale ehealthcare systems, challenges remain with respect to ensuring the efficiency and privacy of these queries. In this study, we construct an efficient and privacy-preserving similar EMR query scheme to help patients find similar EMRs to reference in a large-scale ehealthcare system. Specifically, we propose a coarse-grained query method based on a binary decision tree to find a set of EMRs corresponding to the patient's set of medical-symptom keywords. We also design a fine-grained query method to find similar EMRs that meet the threshold set by the patient. A detailed security analysis shows that the proposed scheme is secure. The efficiency of the proposed method in a large-scale ehealthcare system is verified experimentally. • An efficient and privacy-preserving similar EMRs query scheme is proposed. • A novel privacy-preserving health symptom keywords match query protocol is designed. • Security analysis demonstrates the security of the proposed scheme. • Experimental simulations show the efficiency of the scheme. [ABSTRACT FROM AUTHOR]

Real-time positioning of a specific object in the big data environment can improve the monitoring and management capacity for network data. For the real-time positioning of the specific object, it is necessary to quickly search the network data representing a specific object and match its pattern strings and compare the corresponding Internet protocol (IP) address of the matched network data with the IP address library in real time, so as to determine the position of the specific object. When a traditional method is used for pattern string matching, it will occupy a lot of memories and network resources, thereby reducing the positioning effect of the specific object in the big data environment. A positioning method for a specific object of high performance and multi-pattern matching based on three indexes in the big data network environment is proposed in this paper. Firstly, the initialization of Modified Wu-Manber (MWM) algorithm was carried out, and the algorithm was used to match the network data continuously. Secondly, the three indexes were used to improve the MWM algorithm, and the real-time and fast positioning of a specific object in the big data environment was completed by the Third Index Modified Wu-Manber (TMWM). The experimental results show that compared with the traditional method, the proposed algorithm reduces the pattern string matching scope of network data representing the specific object, improves the search speed of the specific object, and locates the specific object in the big data environment in an effective and rapid manner. [ABSTRACT FROM AUTHOR]

Summary: In classical public‐key infrastructure (PKI), the certificate authorities (CAs) are fully trusted, and the security of the PKI relies on the trustworthiness of the CAs. However, recent failures and compromises of CAs showed that if a CA is corrupted, fake certificates may be issued, and the security of clients will be at risk. As emerging solutions, blockchain‐ and log‐based PKI proposals potentially solved the shortcomings of the PKI, in particular, eliminating the weakest link security and providing a rapid remedy to CAs' problems. Nevertheless, log‐based PKIs are still exposed to split‐world attacks if the attacker is capable of presenting two distinct signed versions of the log to the targeted victim(s), while the blockchain‐based PKIs have scaling and high‐cost issues to be overcome. To address these problems, this paper presents a secure and accountable transport layer security (TLS) certificate management (SCM), which is a next‐generation PKI framework. It combines the two emerging architectures, introducing novel mechanisms, and makes CAs and log servers accountable to domain owners. In SCM, CA‐signed domain certificates are stored in log servers, while the management of CAs and log servers is handed over to a group of domain owners, which is conducted on the blockchain platform. Different from existing blockchain‐based PKI proposals, SCM decreases the storage cost of blockchain from several hundreds of GB to only hundreds of megabytes. Finally, we analyze the security and performance of SCM and compare SCM with previous blockchain‐ and log‐based PKI schemes. [ABSTRACT FROM AUTHOR]

Traffic classification is a technology for classifying and identifying sensitive information from cluttered traffic. With the increasing use of encryption and other evasion technologies, traditional content- based network traffic classification becomes impossible, and traffic classification is increasingly related to security and privacy. Many studies have been conducted to investigate traffic classification in various scenarios. A major challenge to existing schemes is extending traffic classification technology to a broader space. In other words, most traffic classification work is not universal and can only show great performance on specific datasets. In this article, we present a systematic approach to optimizing feature selection for encrypted traffic classification. We summarize the optional encrypted traffic features and analyze the approaches of feature selection in detail for different datasets. The experimental result demonstrates that our scheme is more accurate and universal than other state-of-the-art approaches. More precisely, our mechanism provides a guideline for future research in the field of traffic classification. [ABSTRACT FROM AUTHOR]

With the advent of medical IoT devices, the types and volumes of medical images have significantly increased. Retrieving of medical images is of great importance to facilitate disease diagnosis and improve treatment efficiency. However, it may raise privacy concerns from individuals, since medical images contain patients' sensitive and private information. Existing studies on retrieval of medical data either fail to protect sensitive information of medical images or are limited to a single image data provider. In this article, we propose a blockchain-based system for medical image retrieval with privacy protection. We first describe the typical scenarios of medical image retrieval and summarize the corresponding requirements in system design. Using the emerging blockchain techniques, we present the layered architecture and threat model of the proposed system. In order to accommodate large-size images with storage-constrained blocks, we capture a carefully selected feature vector from each medical image and design a customized transaction structure, which protects the privacy of medical images and image features. We also discuss the challenges and opportunities of future research. [ABSTRACT FROM AUTHOR]

Currently, decentralized redactable blockchains have been widely applied in IoT systems for secure and controllable data management. Unfortunately, existing works ignore policy privacy (i.e., the content of users' redaction policies), causing severe privacy leakage threats to users since users' policies usually contain large amounts of private information (e.g., health conditions and geographical locations) and limiting the applications in IoT systems. To bridge this research gap, we propose PFRB, a policy-hidden fine-grained redactable blockchain in decentralized blockchain-based IoT systems. PFRB follows the decentralized settings and fine-grained chameleon hash-based redaction in existing redactable blockchains. In addition, PFRB hides users' policies during policy matching such that apart from successful policy matching, users' policy contents cannot be inferred and valid redactions cannot be executed. Some main technical challenges include determining how to hide policy contents and support policy matching. Inspired by Newton's interpolation formula-based secret sharing, PFRB converts policy contents into polynomial parameters and utilizes multi-authority attribute-based encryption to further hide these parameters. Theoretical analysis proves the correctness and security against the chosen-plaintext attack. Extensive experiments on the FISCO blockchain platform and IoT devices show that PFRB achieves competitive efficiency over current redactable blockchains. [ABSTRACT FROM AUTHOR]

Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data. Among all the machine learning models, decision tree models stand out due to their great interpretability and simplicity, and have been implemented in cloud computing services for various purposes. Despite its great success, the integrity issue of online decision tree prediction is a growing concern. The correctness and consistency of decision tree predictions in cloud computing systems need more security guarantees since verifying the correctness of the model prediction remains challenging. Meanwhile, blockchain has a promising prospect in two-party machine learning services as the immutable and traceable characteristics satisfy the verifiable settings in machine learning services. In this paper, we initiate the study of decision tree prediction services on blockchain systems and propose VDT, a Verifiable Decision Tree prediction scheme for decision tree prediction. Specifically, by leveraging the Merkle tree and hash function, the scheme allows the service provider to generate a verification proof to convince the client that the output of the decision tree prediction is correctly computed on a particular data sample. It is further extended to an update method for a verifiable decision tree to modify the decision tree model efficiently. We prove the security of the proposed VDT schemes and evaluate their performance using real datasets. Experimental evaluations show that our scheme requires less than one second to produce verifiable proof. [ABSTRACT FROM AUTHOR]

Wireless sensor networks (WSNs) are composed of sensor nodes with limited energy which is difficult to replenish. In-network data aggregation is the main solution to minimize energy consumption and maximize network lifetime by reducing communication overhead. However, performing data aggregation while preserving data confidentiality and integrity is challenging, because adversaries can eavesdrop and modify the aggregation results easily by compromised aggregation nodes. In this paper, we propose an efficient confidentiality and integrity preserving aggregation protocol (ECIPAP) based on homomorphic encryption and result-checking mechanism. We also implement ECIPAP on SimpleWSN nodes running TinyOS. Security and performance analysis show that our protocol is quite efficient while persevering both aggregation confidentiality and integrity. [ABSTRACT FROM AUTHOR]

• This paper proposes a novel multivariate dictionary learning framework. • A coherence enhancement term to improve the coherent representing of the coding coefficients under different resolutions. • A multivariate dictionary optimization method to solve dictionaries involving the calculation of fractional norm. • The proposed method achieves the state-of-the-art performance on several benchmark datasets. Off-the-shelf dictionary learning algorithms have achieved satisfactory results in small sample face recognition applications. However, the achieved results depend on the facial images obtained at a single resolution. In practice, the resolution of the images captured on the same target is different because of the different shooting equipment and different shooting distances. These images of the same category at different resolutions will pose a great challenge to these algorithms. In this paper, we propose a Joint Coupled Representation and Homogeneous Reconstruction (JCRHR) for multi-resolution small sample face recognition. In JCRHR, an analysis dictionary is introduced and combined with the synthetic dictionary for coupled representation learning, which better reveals the relationship between coding coefficients and samples. In addition, a coherence enhancement term is proposed to improve the coherent representation of the coding coefficients at different resolutions, which facilitates the reconstruction of the sample by its homogeneous atoms. Moreover, each sample at different resolutions is assigned a different coding coefficient in the multi-dictionary learning process, so that the learned dictionary is more in line with the actual situation. Furthermore, a regularization term based on the fractional norm is drawn into the dictionary coupled learning to remove the redundant information in the dictionary, which can reduce the negative impacts of the redundant information. Comprehensive results demonstrate that the proposed JCRHR method achieves better results than the state-of-the-art methods, on several small sample face databases. [ABSTRACT FROM AUTHOR]

Cloud platforms provide a low-cost and convenient way for users to share data. One important issue of cloud-based data sharing systems is how to prevent the sensitive information contained in users' data from being disclosed. Existing studies often utilize cryptographic primitives, such as attribute-based encryption and proxy re-encryption, to protect data privacy. These approaches generally rely on a centralized server which may cause a single point of failure problem. Blockchain is known for its ability to solve such a problem. Some blockchain-based approaches have been proposed to realize privacy-preserving data sharing. However, these approaches did not fully explore the auditability provided by the blockchain. The dishonest cloud server can share data with a requester without notifying the data owner or being logged by the blockchain. In this paper, we propose a blockchain-based privacy-preserving data sharing system with enhanced auditability. The proposed system follows the idea of hybrid encryption to protect data privacy. The data to be shared are encrypted with a symmetric key, and the symmetric key is encrypted with a joint public key which is the sum of multiple blockchain nodes' public keys. Only if a data requester is authorized, the blockchain nodes will be triggered to execute a verifiable key switch protocol. By using the output of the protocol, the data requester can get the plaintext of the symmetric key. The blockchain nodes participate in both the authorization process and the key switch process, which means the behavior of the data requester is witnessed by multi-parties and is auditable. We implement the proposed system on Hyperledger Fabric. The simulation results show that the performance overhead is acceptable. [ABSTRACT FROM AUTHOR]

With the development of 5G, the wireless Internet of Things (IoT) has become possible; how to provide privacy protections for the communication of IoT devices in a more vulnerable wireless transmission environment is a huge challenge. Thus, steganography is introduced as a safe and effective technology. Blockchain systems have been widely used in the area of steganography. Several works attempted to embed covert data into transactions in public blockchain systems such as Bitcoin, Ethereum and Monero. However, most of them merely focus on putting covert data into certain fields in transactions based on cryptographic algorithms. In this paper, a Covert Transaction Recognition (CTR) model is proposed by the Text Convolutional Neural Networks and Back Propagation Neural Networks. When utilizing the covert data-embedded field for recognizing, our CTR model can attain 0.79 precision and 0.83 recall on average for seven covert transaction construction schemes. The precision and recall can increase by at most 43 and 47%, respectively, if other unembedded fields were additionally exploited for recognition. We further propose a Practical Covert Transaction Construction (PCTC) model. This model fixes the contents in the embedded fields of the constructed transactions, and generates the contents in other fields using Generative Adversarial Networks. Experimental results demonstrated that the precision and recall are greatly decreased when identifying the covert transactions generated by our PCTC model. The data underlying this article are available in 'covert-transaction-model', at https://github.com/1997mint/covert-transaction-model. [ABSTRACT FROM AUTHOR]

Edge-Integrated Internet-of-Vehicles (IoV) sinks service to edge nodes, which responses quickly to vehicles' requests and alleviates the burden of cloud servers. In such scenario, the entities of IoV need to be mutually authenticated since potential attacks can impersonate edge nodes to send false instructions to vehicles, or impersonate legitimate service subscribers to get free-ride IoV services. Furthermore, due to the high mobility characteristics of vehicles, frequent authentication is required between vehicles and edge nodes. We hope that the authentication process can be conducted efficiently to ensure the continuous service. Existing works cannot balance the security and efficiency well. This is still an issue worthy of discussion. In this article, we propose SEA, a secure and efficient blockchain-assisted authentication scheme for IoV. SEA achieves mutual authentication among vehicles, edge nodes and cloud servers. Specifically, the cloud server is only involved when vehicles are initially authenticated. And edge nodes realize the authentication of vehicles by querying the authentication result recorded by cloud on the blockchain, which significantly reduces the cryptographic computation overhead and eliminates network communication delay. Besides, session keys between any two entities involved are negotiated, which can secure sensitive data of vehicles. Extensive experiments have been conducted to show the security and efficiency of SEA. [ABSTRACT FROM AUTHOR]

Authentication in vehicular ad-hoc network (VANET) is still a research challenge, as it requires not only secure and efficient authentication, but also privacy preservation. In this paper, we proposed a lightweight and efficient authentication scheme (LESPP) with strong privacy preservation for secure VANET communication. The proposed scheme utilizes self-generated pseudo identity to guarantee both privacy preservation and conditional traceability, and it only requires a lightweight symmetric encryption and message authentication code (MAC) generation for message signing and a fast MAC re-generation for verification. Compared with currently existing public key based schemes, the proposed scheme significantly reduces computation cost by $$10^2$$ - $$10^3$$ times and decreases communication overhead by 41.33-77.60 %, thus achieving resilience to denial of service (DoS) attack. In LESPP, only key management center can expose a vehicle's real identity from its pseudo identity, therefore, LESPP provides strong privacy preservation so that the adversaries cannot trace any vehicles, even if all roadside units are compromised. Furthermore, vehicles in LESPP need not maintain certificate revocation list (CRL), so any CRL related overhead is avoided. Extensive simulations reveal that the novel scheme is feasible and has an outstanding performance of nearly 0 ms network delay and 0 % packet loss ratio, which are especially appropriate for realtime emergency event reporting applications. [ABSTRACT FROM AUTHOR]

HDFS faces several issues when it comes to handling a large number of small files. These issues are well addressed by archive systems, which combine small files into larger ones. They use index files to hold relevant information for retrieving a small file content from the big archive file. However, existing archive-based solutions require significant overheads when retrieving a file content since additional processing and I/Os are needed to acquire the retrieval information before accessing the actual file content, therefore, deteriorating the access efficiency. This paper presents a new archive file named Hadoop Perfect File (HPF). HPF minimizes access overheads by directly accessing metadata from the part of the index file containing the information. It consequently reduces the additional processing and I/Os needed and improves the access efficiency from archive files. Our index system uses two hash functions. Metadata records are distributed across index files using a dynamic hash function. We further build an order-preserving perfect hash function that memorizes the position of a small file's metadata record within the index file. • Direct metadata access using perfect hashing. • Archive base solution to HDFS small files problems. • Small files access performance optimization using hashing techniques. • Dynamic index files. [ABSTRACT FROM AUTHOR]

A remarkable increase of personalized healthcare services has been powered by the broad implementation of the networks and their ramifications. The core value of using personalized healthcare systems is to create customized medical service offerings and establish interconnections between patients and physicians. However, the treatment delivery for individual patients is encountering a challenge in effectively creating plans in which medical services can be retrieved from multiple sources at various costs. This article focuses on the problem of minimizing total cost of service retrieval and proposes an approach that uses intelligent agents to dynamically make service retrieval strategies. The approach is called the Smart Treatment for Personalized Healthcare (STPH) model, which is designed to produce an optimal solution to minimizing total cost on the intelligent agent. The performance of the proposed approach has been validated by experimental evaluations. [ABSTRACT FROM AUTHOR]