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Symmetric Searchable Encryption (SSE) schemes facilitate searching over encrypted data, and have been extensively explored to improve function, efficiency or security. There are, however, additional functions that we need to consider in a real-world setting. For example, forward and backward privacy are required to adequately secure newly added documents and deleted documents in Dynamic SSE (DSSE) schemes, and support boolean search (that allows users to search over encrypted data using basic boolean operations) to achieve improved efficiency and retrieval accuracy. Therefore, in this paper we first construct the Verifiable Boolean Search over encrypted data (VBS), and then improve VBS to achieve Forward and Backward privacy (VBS-FB). Finally, we formally prove the security of our proposed schemes, and evaluate their performance using real-world datasets.

Searchable Symmetric Encryption (SSE), which enables users to search over encrypted data without decryption, has gained increasing attention from both academic and industrial fields. However, existing SSE schemes either have low search efficiency or cannot support multi-keyword search, dynamic updates, and result verification simultaneously. To solve these problems, we propose a Verifiable and Dynamic Multi-keyword Search (VDMS) scheme over encrypted data by using the bitmap and RSA accumulator, which provides multi-keyword search over encrypted data in an efficient, verifiable and updated way. The bitmap is used as a data structure to build the indexes, which improves the search efficiency and reduces the storage space of the indexes. The RSA accumulator and bitmap are combined to verify the correctness of results. Formal security analysis proves that our VDMS is adaptively secure against Chosen-Keyword Attacks (CKA), and empirical experiments using a real-world dataset demonstrate that our VDMS is efficient and feasible in practical applications.

In cloud data sharing systems, Searchable Encryption (SE) schemes ensure data confidentiality with retrieving, but it faces several issues in practice. First, most of the previous Ciphertext-Policy Attribute-Based Keyword Search (CP-ABKS) systems enable users to initiate search requests with a single keyword, which results in many inaccurate results to be returned, thereby wasting computing and bandwidth resources. Second, untrusted cloud servers may return a small portion of incomplete search results to compress communication overhead. Besides, most CP-ABKS schemes only support an unshared multi-owner setting, which incurs a large amount of computational and storage overhead. Furthermore, when the keyword space is a polynomial, most of the previous schemes suffer from offline keyword guessing attacks. To address these issues, we focus on a multi-keyword search scheme which supports the verification of search results without losing efficiency by combining Ciphertext Policy Attribute-Based Encryption (CP-ABE) technology under the shared multi-owner mechanism. We show the security of our scheme, which achieves selective security against offline keyword guessing attacks and guarantees the unforgeability of signatures. The comparison of experimental results illustrates that our scheme is effective and enjoys superior functionalities than the most relevant solutions.

In privacy-preserving cross-device federated learning, users train a global model on their local data and submit encrypted local models, while an untrusted central server aggregates the encrypted models to obtain an updated global model. Prior work has demonstrated how to verify the correctness of aggregation in such a setting. However, such verification relies on strong assumptions, such as a trusted setup among all users under unreliable network conditions, or it suffers from expensive cryptographic operations, such as bilinear pairing. In this paper, we scrutinize the verification mechanism of prior work and propose a model recovery attack, demonstrating that most local models can be leaked within a reasonable time (e.g., 98% of encrypted local models are recovered within 21 h). Then, we propose VerSA, a verifiable secure aggregation protocol for cross-device federated learning. VerSA does not require any trusted setup for verification between users while minimizing the verification cost by enabling both the central server and users to utilize only a lightweight pseudorandom generator to prove and verify the correctness of model aggregation. We experimentally confirm the efficiency of VerSA under diverse datasets, demonstrating that \textsc{VerSA} is orders of magnitude faster than verification in prior work.

At present, under the condition of privacy-preserving, vertical federated learning (VFL) has played an important role in training the machine learning models in the application scenarios, such as medical prediction, fraud detection, in which the data is distributed vertically. Random forest is one of the most widely used machine learning methods in VFL, which has the advantages of strong predictive performance, availability, and the ability to parallel tasks. However, current research on privacy-preserving of vertical federated random forest is limited, and none of them can achieve the application level of security, that is, in a system where users are dynamically changing, not only privacy can be preserved, but also data integrity can be verified. Therefore, we propose a verifiable privacy-preserving scheme (VPRF) based on vertical federated random forest, in which the users are dynamic change. Firstly, we design homomorphic comparison and voting statistics algorithms based on multi-key homomorphic encryption for privacy preservation. Then, we propose a multi-client delegated computing verification algorithm to make up for the disadvantage that the above algorithms cannot verify data integrity. Finally, we used the datasets in UCI machine learning warehouse to evaluate the propose scheme. The experiment results indicate that our scheme is more efficient than the existing ones that can achieve the same security level.

To ensure the security of images outsourced to the malicious cloud without affecting searchability on such outsourced (typically encrypted) images, one could use privacy-preserving Content-Based Image Retrieval (CBIR) primitive. However, conventional privacy-preserving CBIR schemes based on Searchable Symmetric Encryption (SSE) are not capable of supporting efficient fine-grained access control and result verification simultaneously. Therefore, in this paper, we propose a Verifiable Fine-grained encrypted Image Retrieval scheme in the Multi-owner multi-user settings (VFIRM). VFIRM first utilizes a novel polynomial-based access strategy to provide efficient fine-grained access control. Then, it employs the dual secure k-nearest neighbor technique to distribute distinct keys to different data owners and data users, and finally implements an adapted homomorphic MAC technique to check the correctness of search results. Our formal security analysis shows that VFIRM is non-adaptive semantic secure if the clients search key is generated randomly and keeps in secret. Our empirical experiments using two real-world datasets (i.e., Caltech101 and Corel5k) demonstrate the practicality of VFIRM.

Ranked keyword search has gained increasing attention due to its attractive advantages such as flexibility and accessibility. However, most existing ranked keyword search schemes ignore the semantic associations between the documents and queries. To solve this challenging issue in cloud-assisted edge computing, we first design the Semantic-aware Ranked Multi-keyword \textbf{S}earch (SRMS) scheme by adopting the Latent Dirichlet Allocation (LDA) topic model and the Chinese Remainder Theorem (CRT)-based secret sharing mechanism. Considering that the cloud server may be malicious, we implement a basic verification mechanism in SRMS to verify the correctness and completeness of search results and extend this verification mechanism in cloud-assisted edge computing scenarios. Formal security analysis proves that SRMS and extended result verification mechanisms are secure in both the known ciphertext model and the known background model. Extensive experiments using the real-world dataset demonstrate that SRMS is efficient and feasible in practical applications.

Linear Regression (LR) is a classical machine learning algorithm which has many applications in the cyber physical social systems (CPSS) to shape and simplify the way we live, work and communicate. This paper focuses on the data analysis for CPSS when the Linear Regression is applied. The training process of LR is time-consuming since it involves complex matrix operations, especially when it gets a large scale training dataset In the CPSS. Thus, how to enable devices to efficiently perform the training process of the Linear Regression is of significant importance. To address this issue, in this paper, we present a secure, verifiable and fair approach to outsource LR to an untrustworthy cloud-server. In the proposed scheme, computation inputs/outputs are obscured so that the privacy of sensitive information is protected against cloud-server. Meanwhile, computation result from cloud-server is verifiable. Also, fairness is guaranteed by the blockchain, which ensures that the cloud gets paid only if he correctly performed the outsourced workload. Based on the presented approach, we exploited the fair, secure outsourcing system on the Ethereum blockchain. We analysed our presented scheme on theoretical and experimental, all of which indicate that the presented scheme is valid, secure and efficient.

Internet of Vehicles (IoV) has been sought as a solution to realize an Intelligent Transportation System (ITS) for efficient traffic management. Data driven ITS requires learning from vehicular data and provide vehicles with timely information to support a wide range of safety and infotainment ITS applications. IoV is vulnerable to multitude of cyber-attacks and privacy concerns. Feder-ated Learning (FL) is on the verge of delivering the collab-orative learning by exchanging learning model parameters instead of actual data which is expected to provide privacy in IoV. However, despite featuring an inherently secure and privacy-preserving framework, FL is still vulnerable to poisoning and reverse engineering attacks. Blockchain technology (BC) has already demonstrated a zero-trust, fully secure, distributed, and auditable information recording and sharing paradigm. In this paper, we present a practical prospect of blockchain empowered federated learning to realize fully secure, privacy preserving and verifiable FL for the IoV that is capable of providing secure and trustworthy ITS services.

Electronic-voting(e-voting), compared with paper voting, has advantages in several aspects. Among those benefits, the ability to audit the electoral process at every stage is one of the most desired features. In Eurocrypt2015, Kiayias et al. proposed an E2E verifiable e-voting system which first provides E2E verifiability without relying on external sources of randomness or the random oracle model. The main advantage of the system is that election auditors need only the election transcript and the feedback from the voters to pronounce the election process unequivocally valid. Unfortunately, their system comes with a huge performance and storage penalty for the election authority (EA) compared to other e-voting systems such as Helios. The reason is the EA forms the proof of tally results. It is required to precompute several ciphertexts. The performance penalty on the EA appears to be intrinsic: voters cannot compute an enciphered ballot themselves because it seems unprovable. In this work, we construct a new e-voting system that retains strong E2E characteristics while eliminating the performance and storage penalty of the EA. Our construction is practical and has a similar performance to Helios. The privacy of our construction relies on the SXDH assumption over bilinear groups via complexity leveraging.

To ensure the proper functioning and performance of Industrial grade IoT devices (IIoT) in Industry 4.0 networks, it is critical to identify the capabilities and malfunctions of their component devices (e.g., sensors, actuators, and controllers) and detect potential misbehavior arising due to cyber-attacks, and misconfiguration. We envision future IoT devices embed behavioral profiles through Security-by-Contract (SxC) that are easy to validate and verify against network security policies; manufacturers to provide Manufacturer Usage Description (MUD) profiles as a manifest for the devices to signal to the network what sort of access and network functionality they require to properly function. We design authentication in the IoT onboarding process, employ blockchains to a verifiable and immutable repository to store this network manifests, that are signed and verifiable with SxC based smart contracts by the device manufacturer, or industry authority. The integrated framework combines Blockchains and SxC security contracts, MUD-based behavioral fingerprinting, and Software-Defined-Networking (SDN) for managing the security of IIoT ecosystems. Finally, the proposed scheme is validated in a simulated IoT environment on various performance parameters.

Cloud-assisted Internet of Things (IoT) is increasingly prevalent used in various fields, such as the healthcare system. While in such a scenario, sensitive data (e.g., personal electronic medical records) can be easily revealed, which incurs potential security challenges. Thus, Symmetric Searchable Encryption (SSE) has been extensively studied due to its capability of supporting efficient search on encrypted data. However, most SSE schemes require the data owner to share the complete key with query users and take malicious cloud servers out of consideration. Seeking to address these limitations, in this paper we propose a Verifiable Privacy-preserving data Search scheme with Limited key-disclosure (VPSL) for cloud-assisted Internet of Things. VPSL first designs a trapdoor generation protocol for obtaining a trapdoor with disclosing limited key information and without revealing plaintext query points to others. Then, VPSL provides an efficient result verification and search processing by employing the Merkle hash tree structure and k-means clustering technique, respectively. VPSL is secure against the level-2 attack. Finally, an enhanced VPSL (called VPSL+) resisting the level-3 attack is constructed by introducing the random splitting technique. Empirical experiments demonstrate the accuracy and efficiency of VPSL or VPSL+ using real-world datasets.

This article develops an adaptive observation-based efficient reinforcement learning (RL) approach for systems with uncertain drift dynamics. A novel concurrent learning adaptive extended observer (CL-AEO) is first designed to jointly estimate the system state and parameter. This observer has a two-time-scale structure and does not require any additional numerical techniques to calculate the state derivative information. The idea of concurrent learning (CL) is leveraged to use the recorded data, which leads to a relaxed verifiable excitation condition for the convergence of parameter estimation. Based on the estimated state and parameter provided by the CL-AEO, a simulation of experience-based RL scheme is developed to online approximate the optimal control policy. Rigorous theoretical analysis is given to show that the practical convergence of the system state to the origin and the developed policy to the ideal optimal policy can be achieved without the persistence of excitation (PE) condition. Finally, the effectiveness and superiority of the developed methodology are demonstrated via comparative simulations.

To manage outsourced encrypted data sharing in clouds, attribute-based proxy re-encryption (ABPRE) has become an elegant primitive. In ABPRE, a cloud server can transform an original recipient's ciphertext to a new one of a shared user's. As the transformation is computation consuming, a malicious cloud server may return an incorrect re-encrypted ciphertext to save its computation resources. Moreover, a shared user may accuse the cloud server of returning an incorrect re-encrypted ciphertext to refuse to pay the cost of using the cloud service. However, existing ABPRE schemes do not support a mechanism to achieve verifiability and fairness. In this paper, a novel verifiable and fair attribute-based proxy re-encryption (VF-ABPRE) scheme is introduced to support verifiability and fairness. The verifiability enables a shared user to verify whether the re-encrypted ciphertext returned by the server is correct and the fairness ensures a cloud server escape from malicious accusation if it has indeed conducted the re-encryption operation honestly. Additionally, we conduct a performance experiment to show the efficiency and practicality of the new VF-ABPRE scheme.

Human Cyber-physical Systems (HCPSs) provide accurate and high-quality services for industry 5.0. In HCPSs, machine learning (ML) prediction provides reliable prediction results for users based on matured models, while security and privacy protection are considerable issues. Based on the modified Okamoto-Uchiyama homomorphic encryption, we propose a verifiable privacy-preserving machine learning prediction scheme for the edge-enhanced HCPSs, which outputs the verifiable prediction results for users without privacy leakage. A batch of prediction results can be verified at one time, which improves the efficiency of verification. Security analysis shows that our scheme protects the privacy of inputs, ML models and prediction results. The experiment results demonstrate that the edge computing architecture greatly alleviates the computational burden of the cloud server. Furthermore, compared with other related schemes, our scheme shows the best execution efficiency. The batch verification can optimize the performance by about 15% compared with single verification on the same scale.

To secure the sensitive information, EMRs usually should be encrypted before being stored on the cloud. However, the existing searchable encryption schemes that are used to encrypt the entire EMRs can hide the sensitive information, but results in the shared EMRs unable to be used by researchers. In addition, if the queried and extracted EMRs are incorrect, it will lead to misdiagnosis even endanger patients' life. In order to solve the above problems, we propose a verifiable keyword search scheme supporting sensitive information hiding for the cloud-based healthcare sharing system. The sensitive information is encrypted while other contents in EMR can be shared among users in this scheme. The doctors and researchers can quickly perform search operations based on keyword to extract the EMRs they requires. But the sensitive information is hidden for the researchers. Furthermore, the correctness of EMRs can be verified when they are extracted from the cloud.

The application of blockchain to Vehicular Edge Computing (VEC) has attracted significant interests. As the Internet of Things plays an essential and fundamental role for data collecting, data analyzing, and data management in VEC, it is vital to guarantee the security of the data. However, the resource-constraint nature of edge node makes it challenging to meet the needs to maintain long life-cycle IoT data since vast volumes of IoT data quickly increase. In this paper, we propose Acce-chain, a storage-elastic blockchain based on different storage capacities at the edge. Acce-chain supports re-write operation to re-write the historical block with a newly generated block without breaking the hash links between the blocks. As a result, Acce-chain ensures that the hot data can be efficiently accessed at the edge without incurring much communication costs or increasing the total size of the chain. To guarantee the security of the re-write process, we propose a new cryptographic primitive named Dynamic Threshold Trapdoor Chameleon Hash (DTTCH). To guarantee the verifiability of query operation, we design a novel storage structure named HybridStore to ensure the verifiable query for on-chain/off-chain IoT data. As a result, Acce-chain achieves both authorized re-write and verifiable query simultaneously. We provide security analysis for the DTTCH scheme and the IoT data query algorithms. We evaluate Acce-chain through experiments and the results show that the performance of the re-write operation is feasible in real-world VEC settings, and the query efficiency can achieve up to several magnitudes better than which of the baseline. The results also demonstrate that Acce-chain can provide high service quality for the latency-sensitive VEC systems.

Advanced Industrial Internet-of-Things (IIoT), such as smart grids, 5G-enabled unmanned aerial vehicles (UAV), and supply chain 4.o, can be used to facilitate smart management. Nevertheless, IIoT systems generate huge amounts of data that need to be outsourced to the cloud for storing and providing real-time search facilities to end-users. Outsourcing IIoT data to a third-party cloud service provider (CSP) introduces several data privacy and integrity issues related to verifying the reliability of users' queries and aggregated outcomes. In this article, we propose a blockchain-based framework for provisioning a privacy-preserving and verifiable query facility to end-users in IIoT systems. The framework uses blockchain to store IoT data as on-chain data and the cloud to store extensive data (e.g., image) as off-chain data and provisioning search services to users by executing a query in both on-chain and off-chain data and generating an aggregated result. Besides, it introduces a new privacy-preserving query mechanism for ensuring sensitive data privacy during query execution. A data owner encrypts both on-chain and off-chain data in the privacy-preserving query mechanism before sending it to the blockchain and cloud. A CSP can perform search operations on the encrypted on-chain and off-chain data to ensure sensitive data privacy. A multisignature-powered query verification model is also built for the blockchain. The query verification model allows each blockchain node to endorse the query result individually and a user to verify the endorsement of the query result before use. The experiments revealed the high efficiency and scalability of the proposed framework. 2005-2012 IEEE. Scopus

The advent of electronic health record (EHR) system has greatly promoted the development of medical services, and its security and privacy are emerging as major concerns. The verifiable database (VDB), where a user outsources his large database to a cloud server and makes queries once he needs certain data, is proposed as an efficient updatable cloud storage model for resource-constrained users. The security goals of VDB include both the data storage integrity and the query result correctness. To improve system efficiency, most existing VDB schemes utilize proof reuse and the technique of proof updating to prove correctness of the query results. However, it ignores the "real-time" of proof generation, which results in an overhead that the user has to perform extra process (e.g. auditing schemes) to check storage integrity. In this paper, we propose a publicly verifiable updatable EHR database scheme that supports privacy-preserving and batch integrity auditing. We modify the existing functional commitment (FC) scheme for the VDB design and construct a concrete FC under the computational l-BDHE assumption. In addition, the use of an efficient verifier-local revocation group signature scheme makes our scheme support dynamic group member operations, and gives nice features, such as traceability and non-frameability.

Most of the organizations using the cloud-based data sharing platforms are multi-group in nature. The existing directly revocable attribute-based encryption (ABE) schemes though seem to be a good fit, but they fail to provide any effective solution for secure multi-group data sharing scenarios. To bridge this gap, we first propose Revocable ABE with Verifiable Outsourced decryption (ReVO-ABE)- a directly revocable collusion-resistant ABE scheme that allows any number of user revocation and joining without affecting the secret membership keys of the nonrevoked users. Based on ReVO-ABE, we build a Dynamic Multi-Group Secure Data Sharing scheme called DMG-SDS. For operations that are exclusive to multi-groups like group merge and split can be performed without affecting the attribute secret keys or membership keys of the nonrevoked users, which is not possible with any of the existing schemes. Our proposed scheme meets the necessary security requirements, and the performance assessment shows that it has much better performance benefits when compared with most the recent competitive schemes.

Despite increasingly emerging applications, a primary concern for blockchain to be fully practical is the inefficiency of data query. Direct queries on the blockchain take much time by searching every block, while indirect queries on a blockchain database greatly degrade the authenticity of query results. To conquer the authenticity problem, we propose a Verifiable Query Layer (VQL) that can be deployed in the cloud to provide both efficient and verifiable data query services for blockchain systems. The middleware layer extracts data from the underlying blockchain system and efficiently reorganizes them in databases. To prevent falsified data from being stored in the middleware, a cryptographic fingerprint is calculated based on each constructed database. The database fingerprint will be first verified by miners and then written into the blockchain. Moreover, public users can verify the entire databases or several databases that interest them in the middleware layer. We implement VQL together with the verification schemes and conduct extensive experiments based on a practical blockchain system. The evaluation results demonstrate that VQL can efficiently support various data query services and guarantee the authenticity of query results for blockchain systems.

In a ubiquitous environment enclosing cooperative Internet of Things (IoT) devices, individuals, and entities, Digital Identity Management (DIM) becomes critical and challenging. DIM pertains to device identities authentication and verification to enable trustworthy service exchange, data collection, and decision making. DIM is the supporting pillar for all online services and the foundation for security and authentication mechanisms. Due to the extreme heterogeneity, scale, and configuration complexity of such environments, enabling trustworthy DIM is crucial and seriously challenging. In an IoT context, devices use local (Digital Identities) DIs stored within a tamper-proof unit and verified by a centralized authority for authentication. The recent attacks on IoT systems showed how vulnerable such a design is. It is also an inherent problem that influences humans. From that, Self-Sovereign Identity (SSI) has emerged as a decentralized DIM approach embracing the concept of portable selfpossession identity. SSI was presented to decouple the DI from the owner to enable large-scale cooperation. However, DI storage and verification still occur on the device and in a centralized manner. Utilizing a local single-point-of-failure storage memory for verifiable credentials is one of the considerable drawbacks in contemporary SSI. In this regard, this paper introduces DTSSIM, a novel Decentralized Trustworthy Self-Sovereign Identity Management framework. DT-SSIM integrates the secret share scheme with the Blockchain-based smart contracts technologies to provide transparent and trustworthy SSI-based digital identity management services for IoT. Storing IoT identity credentials outside the devices’ local storage preserves the identity credentials from being tampered with or misused. Evaluations and discussions show the resiliency assessment of the system and the cost and estimated running times for verification processes in DTSSIM.

Contemporarily, fast development of computing, communication and storage technology has revolutionized the way that various data-based applications reach massive data from underlying sensor networks. However, such process also raises two challenging but critical issues: trustworthy and privacy issue for data collectors. Therefore, this paper pro-poses a novel system, which is designed over the distributed mobile edge network to sufficiently exploit advantages of blockchain and differential privacy to collect trustworthy data and protect privacy for data collectors. Firstly, to im-prove trustworthy of data collections, a new consensus mechanism is proposed for blockchain-based data collection structure, which comprehensively incorporates trustworthy, collection contribution, and throughput together to prefer data collectors for the next block. Secondly, with assistance of fully trusted devices, a verifiable trustworthy evaluation strategy is designed to accurately compute the trustworthy for data collec-tors. Thirdly, we enforce differential privacy on the data stored in global blockchain maintained by the cloud server to protect privacy for data collectors without influencing data availability. Finally, both theoretical analyses and experi-mental results prove that the proposed system comprehen-sively improves performance of data collections in distributed network without adding any additional cost for cloud server, compared to other schemes.

Searchable encryption enables users to perform a keyword-based search over encrypted data. Most of the existing searchable encryption schemes are based on the single cloud server model, which has some limitations, such as the single point of failure and malicious insiders. In contrast, the multicloud server model has higher data availability and security. However, directly moving the existing keyword search schemes to the multicloud would cause larger storage and search costs, and the data security cannot be distinctly improved. In addition, different from single cloud, a challenge is how to effectively verify query integrity when some cloud servers return incorrect or incomplete results in a multicloud situation. To ensure the data security, we propose an iterative encryption method to ensure the file privacy even if multiple cloud servers collude with each other. We use Bloom filter to map keywords and construct a tree index for each cloud server to achieve secure and efficient distributed keyword search. To verify the query integrity, we propose a fast verification algorithm to embed the file content and index information into the file ID. The analysis and experiments demonstrate the security and effectiveness of our scheme.

We provide a novel theory of monitoring. A monitor with career concerns oversees an agent and generates verifiable evidence if the agent shirks. The monitor's ability is uncertain and requires costly investment to maintain. The equilibrium features a "slippery slope'': unpunished shirking reveals that the monitor is likely ineffective, encouraging more shirking in the future. The monitor can prevent shirking forever, but instead lets her ability decay, and the agent becomes entrenched. Term limits reduce shirking by curbing the agent's experimentation motive and long-serving monitors accept bribes to hide evidence. The optimal organizational design exploits externalities between multiple agents.

The ubiquitous needs for extracting insights from data are driving the emergence of service providers to offer predictions given the inputs from customers. During this process, it is important and highly nontrivial for the service providers to generate proofs of honest predictions without leaking the key parameters of their trained models. In addition, the customers are usually unwilling to reveal their sensitive inputs. In this paper, we proposed MVP, which enables Machine learning prediction in a Verifiable and Privacy preserving fashion. MVP features the properties of polynomial decomposition and prime-order bilinear groups to simultaneously facilitate oblivious evaluation and batch outcome verification while maintaining function privacy and input privacy. We further instantiated MVP with Support Vector Machines (SVMs) and extensively evaluated its performance for the spam detection task on three practical Short Message Service (SMS) datasets. Our analysis and evaluation results reveal that MVP achieves the desired properties while incurring low computation and communication overhead.

In mobile crowdsensing (MCS), sensing data uploaded by dishonest workers may be false or even malicious. Thus, a reputation management system is often set up by using workers’ historical behaviours to indicate the quality of sensing data. As existing management schemes usually protect reputation update process, reputation scores are generally stored in plaintext, which may destroy the fair bidding property of a MCS system. To address this issue, we propose an anonymous reputation management system based on dual blockchain architecture, where reputation scores are masked. More precisely, one chain is used to store and update reputation scores, and another chain is responsible for publishing tasks and storing task-related data. To anonymously update and verify the reputation scores without affecting their usages in data sensing process, a kind of ring signature and Pedersen commitment are employed in smart contracts. In addition, a Schnorr signature is generated to make the reputation scores verifiable in the MCS system. We implement a prototype system on Hyperledger Fabric, and simulation results are provided for comparisons with two existing schemes.

Mobile cloud storage (MCS) provides clients with convenient cloud storage service. In this paper, we propose an efficient, secure and privacy-preserving mobile cloud storage scheme, which protects the data confidentiality and privacy simultaneously, especially the access pattern. Specifically, we propose an oblivious selection and update (OSU) protocol as the underlying primitive of the proposed mobile cloud storage scheme. OSU is based on onion additively homomorphic encryption with constant encryption layers and enables the client to obliviously retrieve an encrypted data item from the cloud and update it with a fresh value by generating a small encrypted vector, which significantly reduces the client's computation as well as the communication overheads. Compared with previous works, our presented work has valuable properties, such as fine-grained data structure (small item size), lightweight client-side computation (a few of additively homomorphic operations) and constant communication overhead, which make it more suitable for MCS scenario. Moreover, by employing the "verification chunks" method, our scheme can be verifiable to resist malicious cloud. The comparison and evaluation indicate that our scheme is more efficient than existing oblivious storage solutions with the aspects of client and cloud workloads, respectively.

Verifiable databases (VDB) enables the data owner to outsource a huge unencrypted database to the powerful but untrusted cloud such that any client could later retrieve the database and check whether the cloud returns valid records or not. To the best of our knowledge, there is no prior work considering privacy. Besides, they assume that the data owner and the client are fully trusted while they may be semi-honest in the real world. To address these problems, we propose a new primitive called privacy-preserving publicly verifiable database (PPVDB), which not only guarantees the integrity of the query result but also leaks no information. At the end of this protocol, the client can check whether the cloud returns a valid result and learns the query result but nothing else about the database. Besides, the cloud learns nothing about the database and the query, and the data owner does not know which item that the client has queried. Motivated by the comparison among some strawman solutions, we incorporate verifiable computation for the polynomial with oblivious pseudorandom function to construct a PPVDB scheme, which is the first VDB scheme providing stronger security against the malicious cloud and the semi-honest client and data owner.

In this article, we propose Double Edged (DE)-Sword, an incentivized verifiable tag path query scheme. DE-Sword queries tag records stored across a path of participants within an RFID-enabled supply chain in a verifiable way. Different with previous works, DE-Sword works in a dishonest-data owner model in which participants are the owners of tag records and may be dishonest. DE-Sword introduces a novel double-edged reputation incentive mechanism to encourage participants to behave honestly; and couples it with cryptographic primitives to ensure query verifiability. We evaluate DE-Sword through game theory, security analysis, and performance evaluation. The game theory and security analysis shows that DE-Sword guarantees query verifiability. The evaluation results show that DE-Sword incurs low overhead in supply chain systems.

Due to the strong analytical ability of big data, deep learning has been widely applied to model on the collected data in industrial IoT. However, for privacy issues, traditional data-gathering centralized learning is not applicable to industrial scenarios sensitive to training sets, such as face recognition and medical systems. Recently, federated learning has received widespread attention, since it trains a model by only sharing gradients without accessing training sets. But existing researches reveal that the shared gradient still retains the sensitive information of the training set. Even worse, a malicious aggregation server may return forged aggregated gradients. In this paper, we propose the VFL, a verifiable federated learning with privacy-preserving for big data in industrial IoT. Specifically, we use Lagrange interpolation to elaborately set interpolation points for verifying the correctness of the aggregated gradients. Compared with existing schemes, the verification overhead of VFL remains constant regardless of the number of participants. Moreover, we employ the blinding technology to protect the privacy of the privacy gradients. If no more than n-2 of n participants collude with the aggregation server, VFL could guarantee the encrypted gradients of other participants not being inverted. Experimental evaluations corroborate the practical performance of the presented VFL with high accuracy and efficiency.

An Unmanned Aerial Vehicle (UAV) assisted Trustworthy Code Dissemination (UTCD) framework in 5G-enabled intelligent IIoT systems is proposed to select credible Mobile Vehicles (MVs) to disseminate code with opportunistic routing style. In the UTCD framework, a verifiable trust evaluation scheme is proposed to identify the trust of the MVs by sending the UAV to collect the Code Wait to be Verified (CWV) directly from the selected devices (bedrock devices) as evidence. First, a bedrock devices selection scheme is presented by selecting as few bedrock devices as possible to collect as much the CWV. Then, based on the CWV, a complete trust evaluation scheme is proposed to obtain the trust of MVs. Furthermore, a UAV trajectory optimization algorithm is proposed to obtain as much CWV as possible. Finally, comprehensive experiments demonstrate that the proposed scheme out-performs the existing schemes in terms of the efficiency and security of code dissemination.

Searchable encryption (SE) allows cloud tenants to retrieve encrypted data while preserving data confidentiality securely. Many SE solutions have been designed to improve efficiency and security, but most of them are still susceptible to insider Keyword-Guessing Attacks (KGA), which implies that the internal attackers can guess the candidate keywords successfully in an off-line manner. Also in existing SE solutions, a semi-honest-but-curious cloud server may deliver incorrect search results by performing only a fraction of retrieval operations honestly (e.g., to save storage space). To address these two challenging issues, we first construct the basic Verifiable SE Framework (VSEF), which can withstand the inside KGA and achieve verifiable searchability. Based on the basic VSEF, we then present the enhanced VSEF to support multi-keyword search, multi-key encryption and dynamic updates (e.g., data modification, data insertion, and data deletion) at the same time, which highlights the importance of practicability and scalability of SE in real-world application scenarios. We conduct extensive experiments using the Enron email dataset to demonstrate that the enhanced VSEF achieves high efficiency while resisting to the inside KGA and supporting the verifiability of search results.

With the increasing traffic volume, enterprises choose to outsource their middlebox services, such as deep packet inspection, to the cloud to acquire rich computational and communication resources. However, since the traffic is redirected to the public cloud, information leakages, such as packet payload and inspection rules, arouse privacy concerns of both middlebox owner and packet senders. To address the concerns, we propose an efficient verifiable deep packet inspection (EV-DPI) scheme with strong privacy guarantees. Specifically, a two-layer architecture is designed and deployed over two non-collusion cloud servers. The first layer fast filters out most of legitimate packets and the second layer supports exact rule matching. During the inspection, the privacy of packet payload and the confidentiality of inspection rules are well preserved. To improve the efficiency, only fast symmetric crypto-systems, such as hash functions, are used. Moreover, the proposed scheme allows the network administrator to verify the execution results, which offers a strong control of outsourced services. To validate the performance of the proposed EV-DPI scheme, we conduct extensive experiments on the Amazon Cloud. Large-scale dataset (millions of packets) is tested to obtain the key performance metrics. The experimental results demonstrate that EV-DPI not only preserves the packet privacy, but also achieves high packet inspection efficiency.

Verifiable delegated set intersection over outsourced private datasets (VDPSI) enables two parties to outsource their private datasets and delegate the computation of set intersection to the cloud while being able to check the correctness of the result. In this process, the cloud learns nothing about the datasets and the intersection result. However, the existing VDPSI schemes suffer from three substantial shortcomings that limit their use: i) the whole dataset consists of only one subset, ii) they are designed for the static data, and iii) they cannot support other operations. To resolve these problems, we introduce a novel primitive called tag-based VDPSI (TVDPSI), which is designed for the multisubset case where each subset is associated with one single tag for data classification. To protect privacy, the data is encrypted before being resided to the cloud. The tag is implicitly hidden in each encrypted element. As a result, the cloud cannot learn which data belongs to the same subset beyond the intersection set. Besides, the cloud cannot calculate the intersection except under the permissions of data owners. To the best of our knowledge, TVDPSI is the first VDPSI scheme supporting dynamic update and count operations. The detailed performance evaluation and simulation show that our protocol is more practical in cloud computing.

Diverse technologies, such as machine learning and big data, have been driving the prosperity of the Internet of Things (IoT) and the ubiquitous proliferation of IoT devices. Consequently, it is natural that IoT becomes the driving force to meet the increasing demand for frictionless transactions. To secure transactions in IoT, blockchain is widely deployed since it can remove the necessity of a trusted central authority. However, the mainstream blockchain-based IoT payment platforms, dominated by Proof-of-Work (PoW) and Proof-of-Stake (PoS) consensus algorithms, face several major security and scalability challenges that result in system failures and financial loss. Among the three leading attacks in this scenario, double-spend attacks and long-range attacks threaten the tokens of blockchain users, while eclipse attacks target denial of service. To defeat these attacks, a novel bidirectional-linked blockchain (BLB) using chameleon hash functions is proposed, where bidirectional pointers are constructed between blocks. Furthermore, a new Committee Members Auction (CMA) consensus algorithm is designed to improve the security and attack resistance of BLB while guaranteeing high scalability. In CMA, distributed blockchain nodes elect committee members through a verifiable random function. The smart contract uses Shamir’s Secret Sharing scheme to distribute the trapdoor keys to committee members. To better investigate BLB’s resistance against double-spend attacks, an improved Nakamoto’s attack analysis is presented. In addition, a modified entropy metric is devised to measure eclipse attack resistance across different consensus algorithms. Extensive evaluation results show the superior resistance against attacks and demonstrate high scalability of BLB compared with current leading paradigms based on PoS and PoW.

The distribution of personal health records (PHRs) via a cloud server is a promising platform as it reduces the cost of data maintenance. Nevertheless, the cloud server is semi-trusted and can expose the patients’ PHRs to unauthorized third parties for financial gains or compromise the query result. Therefore, ensuring the integrity of the query results and privacy of PHRs as well as realizing fine-grained access control are critical key issues when PHRs are shared via cloud computing. Hence, we propose new personal health records sharing scheme with verifiable data integrity based on B+ tree data structure and attribute-based signcryption scheme to achieve data privacy, query result integrity, unforgeability, blind keyword search, and fine-grained access control.

A multi-party quantum key distribution protocol based on repetitive code is designed for the first time in this paper. First we establish a classical (t, n) threshold protocol which can authenticate the identity of the participants, and encode the classical key sequence in accordance with this repetitive code. Then unitary transformation of the quantum state sequence corresponding to this encoded sequence is carried out by using the parameters from this (t, n) threshold protocol. Furthermore, we derive two thresholds for whether or not reserving the measured values of the received sequence, and extract the classical subkey sequence from the measured values conforming to these two threshold conditions. This protocol can authenticate the identity of the participant, resist the attack from the internal and external participants, and do not need the decoy state particles when testing the eavesdropper, which is more efficient than the similar protocols, and also saves the quantum resources.

The rapid development of cloud computing and Internet of Things enables widely use of Internet of Vehicles (IoV) and drives a revolutionary change for intelligent transport system. The advent of 5G and fog computing architectures also facilitates applications of IoV and makes the enormous IoV data outsourcing and sharing in cloud and fog more convenient. However, the data outsourcing and sharing will incur many security concerns to IoV including data leakage and breach as the users cannot direct control their data. To guarantee IoV data security, a novel scheme is proposed for IoV data sharing with high efficiency. In our scheme, we utilize ciphertext-policy attribute-based encryption (CP-ABE) to achieve confidentiality and fine-grained access control for IoV data shared in cloud and fog. Nevertheless, as another intrinsic feature in IoV environment, the dynamicity in a group of connected vehicles brings some challenges to our scheme. To deal with this situation, we bring forward a concept of auditable user revocation for CP-ABE to adapt to the dynamic vehicle groups. Moreover, online/offline and verifiable outsourcing techniques are leveraged to improve the efficiency and guarantee the correctness of decryption. At the end, we demonstrate the security and efficiency for our scheme by giving detailed security analysis and performance evaluation with extensive experiments.

As cloud computing and Internet of Things (IoT) are becoming more and more deeply integrated, the new secure challenge for IoT data has attracted great attention. Privacy preserving keyword search, as one important secure technique, achieves search over encrypted cloud-based IoT data. Forward security and verifiability are two important security properties for privacy preserving keyword search for cloud-based IoT data. Forward security makes privacy preserving keyword search schemes for cloud-based IoT data able to resist file injection attack, which is one new attack on privacy preserving keyword search when IoT data is updated. Verifiability makes privacy preserving keyword search schemes for cloud-based IoT data able to ensure the validity of the search results returned by cloud. However, to the best of our knowledge, all of existing privacy preserving keyword search schemes at most achieve forward security and partial verifiability. In order to solve this problem, we explore how to simultaneously achieve the forward security and the full verification for intelligent encrypted data processing in cloud-based IoT and propose the first fully verifiable forward secure privacy preserving keyword search scheme for IoT outsourced data. In order to achieve forward security, we construct the secure index based on the hash chain. In each update, the IoT data owner randomly chooses a string as a new state to generate the new trapdoor. The new trapdoor cannot match previous trapdoors. In order to achieve the full verifiability while still maintaining forward security for intelligent encrypted data processing in cloud-based IoT, we design novel authentication construction called Forward Secure Accumulative Authentication Tag (FSAAT). This kind of tag has the incremental property and supports the verification on the basis of efficient data update. We prove that our scheme is secure and efficient through detailed security analysis and experiments.

Federated Learning (FL) has received widespread attention for its ability to conduct collaborative learning without collecting raw data. Recently, it has became a reality that more accurate model training is achieved through the large-scale deployment of FL on resource-constrained device, where the communication is expensive and clients dropping out is common, such as mobile phone or IoT devices etc. However, shared local gradients make the privacy of local data in FL vulnerable, and the client is easily deceived by the server for the returned forged results. To solve these problems, the existing schemes either only consider the privacy protection requirements under the communication-limited but not involving verifiability, or consider the privacy-protection and verification separately, which incurs expensive computation and communication costs. It is a challenge to design a lightweight verifiable privacy preserving gradient aggregation scheme for large-scale resource-constrained clients under the communication-limited condition. In this paper, we proposed a non-interactive verifiable privacy-preserving FL based on dual-servers (NIVP-DS) architecture, which improves the efficiency and security of the system and is robust to clients dropping out, based on the constraints that the communication overhead between client and server not more than 2 × that of plaintext computation. Based on NIVP-DS, an efficient privacy gradient aggregation scheme is presented by exploiting random matrix coding and secure 2-party computation. The scheme only costs O ( M ) fully linear operation in the client side under the communication constraints. In order to realize the verifiability, a cross-verification method is introduce, which is based on credible matrix exchange to extend the privacy aggregation scheme to a verifiable scheme. The method only costs little additional overhead, meanwhile, guarantees that one dishonest server cannot forge the aggregate results to deceive the honest client, even if it colludes with multiple clients. The effectiveness of NIVP-DS in practice is corroborated by experiments. The results show that the performance of both secure aggregation and verification are efficiency, and the additional overhead of verification is minimal.

In this article, we consider the following cloud storage security problem: A user outsources his/her data to a cloud and later frequently updates the data by adding new data items and deleting existing ones. When the user requests an existing data item from the cloud, the cloud may claim that there is no such data item. This could happen due to cloud management issues, hardware/software failures, hackers, or economic incentives, such as saving cloud’s operation costs. The ability to confirm the data existence/nonexistence when accessing a data item on the dynamic outsourced cloud storage is referred to as dynamic verifiable data access (DVDA). Existing cloud security research works, including encrypted search and proof of storage (i.e., proof of retrievability and proof of data possession), are not sufficient for addressing dynamic verifiable data access. In this article, we present a solution for the dynamic verifiable data access problem. In order to support data dynamics and verifiability, we enhance a hash authentication tree that is build on top of a hash table with a new semantics. With the enhanced hash authentication tree, the proposed protocol supports adding/deleting data in the outsourced storage while satisfying data access verifiability requirement simultaneously. Our open-source experimental evaluation shows that it only takes less than 0.1 ms to verify a data access on a dataset with 400 items using communication cost around 1.7 KB.

Presently many searchable encryption schemes have been proposed for cloud and fog computing, which use fog nodes (or fog servers) to partly undertake some computational tasks. However, these related schemes still retain cloud servers to undertake most computational tasks, which result in large communication costs between edge devices and cloud servers. Therefore, in this paper we propose a self-verifiable attribute-based keyword search scheme for distributed data storage (SV-KSDS) in full fog computing, where each decryption operation on the data required by a user must meet the negotiated decryption rule between fog servers. Our SV-KSDS scheme first provides attribute-based distributed data storage among fog servers through the (w,σ) threshold secret-sharing scheme, where fog servers can provide self-verifiable keyword search and data decryption for terminal users. Compared with the data storage in cloud computing, our scheme extends it to the distributed structure while providing fine-grained access control for distributed data storage through attribute-based encryption. The access control policy of our scheme is constructed on linear secret-sharing scheme, whose security is reduced to the decisional bilinear Diffie-Hellman assumption against chosen-keyword attack and the decisional q-parallel bilinear Diffie-Hellman assumption against chosen-plaintext attack in the standard model. Based on theoretical analysis and practical testing, our SV-KSDS scheme generates less computation and communication costs, which further unloads some computational tasks from terminal users to fog servers so as to reduce computing costs of terminal users.

The celebrated result of Fischer, Lynch and Paterson is the fundamental lower bound for asynchronous fault tolerant computation: any 1-crash resilient asynchronous agreement protocol must have some (possibly measure zero) probability of not terminating. In 1994, Ben-Or, Kelmer and Rabin published a proof-sketch of a lesser known lower bound for asynchronous fault tolerant computation with optimal resilience against a Byzantine adversary: if $n\le 4t$ then any t-resilient asynchronous verifiable secret sharing protocol must have some non-zero probability of not terminating. Our main contribution is to revisit this lower bound and provide a rigorous and more general proof. Our second contribution is to show how to avoid this lower bound. We provide a protocol with optimal resilience that is almost surely terminating for a strong common coin functionality. Using this new primitive we provide an almost surely terminating protocol with optimal resilience for asynchronous Byzantine agreement that has a new fair validity property. To the best of our knowledge this is the first asynchronous Byzantine agreement with fair validity in the information theoretic setting.

With the large-scaled data generated from various interconnected machines and networks, Industrial Internet of Things (IIoT) provides unprecedented opportunities for facilitating data mining for industrial applications. The current IIoT architecture tends to adopt cloud computing for further timely mining IIoT data, however, the openness of security-critical IIoT becomes challenging in terms of unbearable privacy issues. Most existing privacy-preserving data mining (PPDM) techniques are designed to resist honest-but-curious adversaries (i.e., cloud servers and data users). Due to the complexity and openness in IIoT, PPDM is significantly difficult with the presence of malicious adversaries in IIoT who may incur incorrect learned models and inference results. To solve the aforementioned issues, we propose a framework to extend existing PPDM to guard linear regression against malicious behaviors (hereafter referred to as GuardLR). To prevent dishonest computations of cloud servers and inconsistent inputs of data users, we first design a privacy-preserving verifiable learning scheme for linear regression, which guarantees the correctness of learning. In this article, to avoid malicious clouds from returning incorrect inference results, we design a privacy-preserving prediction scheme with lightweight verification. Our formal security analysis shows that GuardLR achieves privacy, completeness, and soundness. Empirical experiments using real-world datasets also demonstrate that GuardLR has high computational efficiency and accuracy.

Today, cloud storage becomes one of the critical services, because users can easily modify and share data with others in cloud. However, the integrity of shared cloud data is vulnerable to inevitable hardware faults, software failures or human errors. To ensure the integrity of the shared data, some schemes have been designed to allow public verifiers (i.e., third party auditors) to efficiently audit data integrity without retrieving the entire users' data from cloud. Unfortunately, public auditing on the integrity of shared data may reveal data owners' sensitive information to the third party auditor. In this paper, we propose a new privacy-aware public auditing mechanism for shared cloud data by constructing a homomorphic verifiable group signature. Unlike the existing solutions, our scheme requires at least t group managers to recover a trace key cooperatively, which eliminates the abuse of single-authority power and provides non-frameability. Moreover, our scheme ensures that group users can trace data changes through designated binary tree; and can recover the latest correct data block when the current data block is damaged. In addition, the formal security analysis and experimental results indicate that our scheme is provably secure and efficient.

Despite exciting progress on cryptography, secure and efficient query processing over outsourced data remains an open challenge. We develop a communication-efficient and information-theoretically secure system, entitled Obscure for aggregation queries with conjunctive or disjunctive predicates, using secret-sharing. Obscure is strongly secure (i.e., secure regardless of the computational-capabilities of an adversary) and prevents the network, as well as, the (adversarial) servers to learn the user's queries, results, or the database. In addition, Obscure provides additional security features, such as hiding access-patterns (i.e., hiding the identity of the tuple satisfying a query) and hiding query-patterns (i.e., hiding which two queries are identical). Also, Obscure does not require any communication between any two servers that store the secret-shared data before/during/after the query execution. Moreover, our techniques deal with the secret-shared data that is outsourced by a single or multiple database owners, as well as, allows a user, which may not be the database owner, to execute the query over secret-shared data. We further develop (non-mandatory) privacy-preserving result verification algorithms that detect malicious behaviors, and experimentally validate the efficiency of Obscure on large datasets, the size of which prior approaches of secret-sharing or multi-party computation systems have not scaled to.

Advanced artificial intelligence techniques, such as the federated learning, has been applied to broad areas, e.g., image classification, speech recognition, smart city, and healthcare. Despite intensive research on the federated learning, existing schemes are vulnerable to attacks and cannot meet the security requirement for real applications. The problem of designing a secure federated learning framework to ensure the correctness of training procedure has not been sufficiently studied and remains open. In this paper, we propose VFChain, a verifiable and auditable federated learning framework based on the blockchain system. First, to provide the verifiability, a committee is selected through the blockchain to collectively aggregate models and record verifiable proofs in the blockchain. Then, to provide the auditability, a novel authenticated data structure is proposed for blockchain to improve the search efficiency of verifiable proofs and support a secure rotation of committee. Finally, to further improve the efficiency, an optimization scheme is proposed to support multiple-model learning tasks. We implement VFChain and conduct extensive experiments by utilizing the popular deep learning model and the public real-world dataset. The evaluation results demonstrate the effectiveness of our proposed VFChain system.