Blockchain has become a superior carrier for data storage due to its decentralization and immutability. How to realize privacy-preserving data sharing and controlled transfer of blockchain data access rights has become a challenging issue. Autonomous path proxy re-encryption allows the delegator to designate a series of delegatees to obtain decryption privileges according to a predefined sequence in a multi-hop manner. However, it faces the hop-skipping issue, where a malicious proxy can directly re-encrypt the ciphertext to the delegatees several hops afterwards, by skipping one or more delegatees in between, destroying the decryption privileges of the skipped delegatees. Furthermore, the intrinsic nature of proxy re-encryption makes it vulnerable to collusion attack, wherein the proxy and a delegatee might collude to construct a decryption device which can decrypt all the delegator’s ciphertexts, posing a serious threat to the delegator’s privacy. To address these challenging issues, we propose a generalized collusion and hop-skipping resistant autonomous path proxy re-encryption for blockchain (CHRAP-PRE). First, we decentralize the proxy’s privileges of re-encrypting ciphertexts to resist collusion attack. Second, we carefully design the decryption token mechanism so that only the person who gets the correct token can do the decryption, which is authorized by all previous persons in the path, thus controlling the decryption privileges to solve the hop-skipping problem. Finally, we formally prove that our proposed CHRAP-PRE achieves IND-HRA security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption, resisting both collusion and hop-skipping attacks. Our comprehensive performance evaluation demonstrates that our scheme offers enhanced security while reducing communication overhead compared to the state-of-the-art.

Blockchain technology, with its characteristics of decentralization, immutability and traceability, provides a new platform and technical means for financial transactions, such as lending systems. There are critical security and efficiency demands: Firstly, due to the high volume of real-time concurrent lending transactions, the system must be highly efficient. Furthermore, to achieve fine-grained accountability for illegal lending approval under regulatory oversight, a weighted threshold architecture is required to enable multi-auditor reviews aligned with hierarchical differences among auditors (e.g., seniority or authority levels), and ensure compliance and fairness. Unfortunately, the existing work of threshold signatures face significant limitations: fully anonymous schemes lead to accountability challenges (e.g., difficulty in tracing malicious actors); node interactions during initialization result in prohibitively high overhead; excessive disclosure of identity information severely compromises signer privacy. These shortcomings render traditional schemes unsuitable for lending transactions in blockchain. In order to address these issues, we firstly propose an efficient fine-grained accountable weighted threshold signature (FAWTS). It enables non-interactive initialization without trusted third parties, achieves fine-grained accountability and introduces dynamic weighted thresholds. Then based on FAWTS, we design a conditional privacy-preserving lending transactions in blockchain, which assigns dynamic weighted thresholds for multiple auditors/lending transactions, enables fine-grained tracing of malicious auditors and achieves non-interactive auditor selection to meet high real-time processing demands. Finally, we formally prove that our proposed schemes achieve both the unforgeability and privacy requirements, while significantly reducing computational and communication overhead under extended functionalities.

The security of distributed systems, especially blockchain-based applications, relies heavily on threshold signature protocols to maintain decentralization. Identifiable aborts are critical in these protocols, enabling detection and attribution of malicious behavior. However, existing threshold EdDSA schemes with identifiable aborts often rely on zeroknowledge proofs, which significantly increase computational overhead. Additionally, these schemes suffer from inefficient abort handling and difficulty defending against DDoS attacks targeting abort mechanisms. Furthermore, weighted threshold signature schemes frequently encounter weight centralization, where high-weight participants dominate decision-making. In this paper, we propose a threshold EdDSA protocol, named EdFROST, which is unforgeable and supports identifiable aborts under a chosen-message attack (IA-CMA), based on FROST3 proposed by Ruffing et al. (ACM CCS '22). Leveraging EdFROST, we present the first weighted threshold EdDSA scheme that considers both the number of participants and arbitrary weight distribution to mitigate the risk of weight centralization. We also design a game-theoretic incentive model, implemented via tamper-proof chaincode, achieving rational identifiable aborts with a unique sequential equilibrium. This model incentivizes honest behavior to maximize individual benefits while ensuring efficient abort handling and resisting DDoS attacks targeting IA. Experimental results demonstrate that the EdFROST and chaincode are efficient and lightweight, making them well-suited for large-scale distributed systems.

Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.

Anonymous credentials are ideal for decentralized systems like blockchains, as they enhance privacy, security, and regulatory compliance while maintaining flexibility and adaptability. These decentralized systems often require features such as weighted threshold issuance and tracing for elasticity of decision-making, fine-grained blindness for user privacy control, and issuer-hiding to reduce potential external threats. However, the latest advancements in anonymous credential schemes cannot meet all of these essential properties for blockchain systems. To address these challenges, we propose weighted threshold anonymous credentials with redactable fine-grained blind signature (WTAC). Firstly, by leveraging unlinkable redactable signatures (URS) and functional encryption techniques, our redactable fine-grained blind signature supports selective disclosure and permits credential issuers to learn a particular function value related to the attributes during issuance without revealing the actual attribute content. Secondly, we utilize weighted ramp secret-sharing (WRSS) and randomizable signatures to achieve weighted threshold anonymous credentials, which are issuer-hiding to both users and verifiers. Moreover, we provide a concrete construction instantiated by the Fiat-Shamir paradigm and demonstrate its application, a privacy-preserving auditable lending system in blockchain scenarios, achieving the integration of user privacy and regulatory compliance. Finally, we give a formal security proof of anonymity, fine-grained blindness, traceability, non-frameability, and issuer-hiding. Performance evaluation shows the practicability and efficiency of our proposed WTAC.

Secure off-chain payment plays an important role in blockchain ecosystems, requiring atomicity guarantees where either all payment channels update their balances or none do. While the Lightning Network achieves this through multi-hop Hash Time-Lock Contracts (HTLCs), a critical flaw persists. Specifically, contract conditions (e.g., hash preimages) are transmitted in plaintext across payment paths. Consequently, malicious intermediate nodes can launch interception attacks by claiming funds from predecessors without releasing the corresponding coins to successors. To mitigate this risk, we propose a secure off-chain payment protocol. Firstly, a new cryptographic primitive, namely ciphertext unlinkable autonomous path proxy re-encryption scheme (CUAP-PRE), is proposed. Unlike traditional multi-hop proxy re-encryption, it prevents malicious nodes by enabling the delegator to designate all delegatees he trusts. In addition, ciphertext unlinkability resists homology inference attacks and delegation path tracing to ensure anonymity. Building on CUAP-PRE, a secure off-chain payment protocol (SOCP) in blockchain is designed with an enhanced multi-hop Hash Time-Lock Contract of the Payment-Channel Network (PCN). The receiver at the end of one path of payment channel can control the decryption rights of payment condition in multi-hop delegation manner with reversed order, to unlock the corresponding bitcoins on hold. We then present formal security proofs to demonstrate that the proposed CUAP-PRE and SOCP are postcompromise secure under the Decisional Bilinear Diffie-Hellman (DBDH) assumption in the random oracle model. Comprehensive evaluations also demonstrate the effectiveness and practicability of our proposal.

Efficient and maliciously secure multiparty private set intersection (mPSI)—especially for variants enabling cardinality (mPSI-CA) or secret shared outputs with low communication overhead—faces ongoing challenges regarding performance and scalability. However, existing approaches are either based on strong security assumptions of non-colluding centers or are relatively expensive in terms of computational and communication overheads. Addressing these limitations, this paper introduces a new suite of protocols and formalizes them in a real/ideal model to prove the security of the protocols. Firstly, our core mPSI protocol achieves malicious security under the standard honest majority model, relying solely on symmetric-key primitives. The approach employs a lightweight, oblivious key-value store (OKVS)-based architecture where each non-pivot party sends only a single message to a designated pivot. This approach, inspired by Nevoet al.(CCS 2021), minimizes client overhead by carefully delegating core computations. We extend this framework to support cardinality (mPSI-CA) and secret sharing (mPSI-SS) functionalities, which require an additional non-collusion assumption among specific parties. We also introduce a Chinese Remainder Theorem (CRT)-based batching technique for parallel mPSI, achieving near-linear communication savings by compressing multiple OKVS structures. This method generally trades higher computational costs (from polynomial operations and CRT) for communication efficiency, but it is highly effective when communication is paramount or when batching numerous instances of small item sets, where encoding computations can be competitive. Finally, our implementation and evaluation of the proposed mPSI and mPSI-CA protocols in both LAN and WAN settings demonstrate their practical advantages. For instance, in the LAN setting depicted (15 parties,t= 7,m= 220), our mPSI protocol is 3.0× faster and uses 2.5× less communication than Nevoet al.(CCS 2021). Against the approach of Gaoet al.(CCS 2024), our maliciously secure protocol is 1.4× faster with comparable communication overhead under weaker assumptions.

The proliferation of mobile internet and sharing economy has catalyzed the emergence of Ride-Sharing Services (RSSs) as a paradigm of spatial crowdsourcing in intelligent transportation. Compared with ride-hailing, RSSs present heightened challenges in security and service quality management due to bidirectional disclosure of trip plans and complex matching logic. Existing secure RSS solutions predominantly operate under semi-honest threat models or suffer from prohibitive computational complexity in service composition. However, ensuring public verifiability of matching outcomes is equally critical to prevent manipulation and ensure accountability in decentralized environments. Moreover, achieving a harmonious trade-off among privacy preservation, public verifiability, and matching efficiency remains an open challenge in RSS systems. This work proposes TBRS, a novelTEE-Blockchain powered privacy-awareRide-Sharing framework, which innovatively addresses three core challenges in service computing: (1) formalizing aninclusive matching modelthat extends traditional identical matching through trajectory region overlap analysis and direction alignment verification; (2) designing anIndex-Preserving Bloom Filter (IP-BF)coupled with Hilbert R-tree spatial indexing, achieving$O(\log n)$matching complexity through computational geometry optimization; (3) implementing a hybrid trusted execution environment via SGX-enhanced consortium blockchain withprivate smart contracts, ensuring verifiable service operations management under malicious threats. The framework demonstrates significant advancements in service performance management through systematic experiments: 2${\times }$$\sim$11${\times }$acceleration in on-chain service composition, 6×$\sim$33× improvement in off-chain computation efficiency, while maintaining over 99% service matching accuracy. These results signify that TBRS effectively breaks the efficiency bottleneck of existing privacy-preserving RSS solutions, making decentralized ride-sharing practical for deployment.

Data duplication within large-scale corpora often impedes large language models' (LLMs) performance and privacy. In privacy-concerned federated learning scenarios, conventional deduplication methods typically rely on trusted third parties to perform uniform deletion, risking loss of informative samples while introducing privacy vulnerabilities. To address these gaps, we propose Federated ReWeighting (FedRW), the first privacy-preserving framework, to the best of our knowledge, that performs soft deduplication via sample reweighting instead of deletion in federated LLM training, without assuming a trusted third party. At its core, FedRW proposes a secure, frequency-aware reweighting protocol through secure multi-party computation, coupled with a parallel orchestration strategy to ensure efficiency and scalability. During training, FedRW utilizes an adaptive reweighting mechanism with global sample frequencies to adjust individual loss contributions, effectively improving generalization and robustness. Empirical results demonstrate that FedRW outperforms the state-of-the-art method by achieving up to $28.78\times$ speedup in preprocessing and approximately $11.42$\% improvement in perplexity, while offering enhanced security guarantees. FedRW thus establishes a new paradigm for managing duplication in federated LLM training.

With the rapid proliferation of Internet of Things (IoT)-enabled healthcare systems, sensitive data such as electronic health records (EHRs) are increasingly outsourced to semi-trusted e-health clouds, raising critical concerns about privacy and verifiability. This paper investigates secure and efficient keyword-based data retrieval in such environments. Key-aggregate searchable encryption (KASE) enables a data owner to share multiple encrypted documents with users using a single compact key, but existing schemes offer limited resistance to offline keyword guessing attacks (KGA) and insufficient protection against keyword privacy leakage. To systematically expose broader vulnerabilities in existing KASE constructions, we present a novel and general attack, termed expanded inside trapdoor attack (EITA), which targets both the security of aggregate keys and the privacy of keywords in unauthorized documents. Furthermore, flexible search methods and result verification are essential for improving KASE performance. To this end, we propose a new KASE scheme that supports conjunctive queries and employs aggregate signatures for verification, while being able to resist KGA and EITA. The security analysis and performance evaluation both demonstrate that our proposed scheme is provably secure and practically efficient in IoT-enabled healthcare settings.

Ring signatures are a crucial cryptographic tool underpinning blockchains and blockchain-based systems, and there are ongoing efforts to design different ring signatures to provide varying properties in a range of applications (e.g., batch verification of numerous ring signatures). In this article, we propose an efficient aggregate ring signature (ARS) scheme for anonymous transaction verification in blockchain. First, a comprehensive definition and the formal security model of our proposed ARS scheme are given, where the true identity of the signer will be protected against both the verifier and other signers contributing to the signature. Furthermore, we present a concrete ARS scheme that can aggregate multiple signatures produced by different signers of the same ring, by exploiting the compressed$\Sigma $-protocol. There are no interactions required among signers and only one single round between each signer and the aggregator. Building on the proposed ARS scheme, we present a confidential transaction (CT) protocol called ARSCT. The latter allows multiple transactions to be aggregated into one transaction. Finally, the formal security proof demonstrates our proposed ARS scheme achieves both anonymity and unforgeability, where signers’ anonymity is protected against both the verifier and the aggregator. Performance evaluations show that in simultaneous multiple signers situation, our scheme outperforms other mainstream ring signature schemes in verification efficiency.

In the distributed computing environment, it is important to efficiently achieve secure access to data. One of the widely applied encryption mechanisms is the multiauthority attribute-based encryption (ABE). However, most existing multiauthority setting schemes were relied on bilinear pairings, which causes the system to bear a relatively large burden in computation overhead. In this article, we proposes a decentralized multiauthority key-policy ABE scheme without bilinear pairings, its security is relied solely on the decisional Diffie-Hellman assumption. It removes the trusted central authority and prevents user collusion attacks. Except for the global coordination between the attribute authorities, any party can simply play the part of a standard attribute authority by generating public keys and issuing corresponding decryption key components for users. The proposed scheme provides heightened security and efficiency compared to the current pairing-free multiauthority ABE scheme, as well as superior computational efficiency when compared to those utilizing bilinear pairings.

Ring signatures are a useful cryptographic technique for anonymous transactions on the blockchain, which allows a user to sign a message on behalf of a group, without revealing which specific member of the group did the signing. However, the anonymity it provides can at times be too powerful and needs to be controlled when used in practice (e.g., on a reliable blockchain anonymous transaction system). In order to address this issue, we propose a linkable, k-times traceable and revocable ring signature (Lk-TRS). Specifically, with respect to the same issue, Lk-TRS not only establishes links among fewer than k signatures of a signer but also enables tracing of the identity when the signer signs k times. Based on our Lk-TRS, we propose a blockchain anonymous transaction system that supports the identity binding of a single user to multiple accounts, as well as the dynamic joining and exiting of users. This ensures the system meets practical requirements and offers runtime flexibility. We prove that our Lk-TRS achieves anonymity, unforgeability, linkability, k-traceability, and exculpability. Compared to the state-of-the-art, our Lk-TRS is more practical for large blockchain systems, maintaining a constant signature size regardless of the ring size.

While techniques such as Homomorphic Encryption (HE) and Intel Software Guard Extensions (SGX) have been leveraged to secure outsourced electronic health record (EHR) computation, there are a number of known limitations (e.g., performance bottlenecks, lack of desired functionalities, or the reliance on a trusted third party). To address these challenging issues, this paper proposes MuEOC, an SGX-based multi-key homomorphic outsourcing computation scheme without a trusted third party. MuEOC is designed to support arbitrary depth multiplication and multi-key setup. In our approach, we first propose an SGX-assisted fully homomorphic encryption scheme (XFHE) under the randomized AGCD assumption. This allows us to replace costly bootstrapping with re-encryption in the enclave, and enable key-switching and Galois automorphism. Using XFHE as a building block, we construct a high-performance multi-key outsourcing computing protocol without the trusted third-party setup. Leveraging SGX, the MuEOC incorporates efficient sub-protocols of secure ciphertext transformation, secure polynomial evaluation, secure inner product, and secure non-linear function evaluation. We also present formal security proof which shows that our proposal guarantees the privacy of input EHR data and medical analysis results under the semi-honest model. Finally, experimental results demonstrate the outstanding efficiency of MuEOC.

Although the global navigation satellite system (GNSS) has been successfully applied in search and rescue operations for locating lost or damaged vehicles due to its significance in precise positioning, there are still challenges. To enhance the accuracy of localization, positioning can be done with the collaborative estimation provided by neighbouring mobile terminals as reference vehicles. Meanwhile, there are security and privacy implications associated with such an approach – e.g., potential for privacy leakage of both the positioning-related data (e.g., positions of reference vehicles, distances between reference vehicles and the target vehicle) and the estimated positions of the target vehicle. Such concerns are important to address in deployments for sensitive applications such as defense. For example, the location information of a damage vehicle on the battlefield should only be securely evaluated by search unit and be successfully decrypted by an authorized set of officers in rescue unit with the required authorization levels. Accordingly, the threshold should be flexibly allocated and changed for types of security surroundings. However, existing techniques of threshold public key homomorphic encryption approaches are not only computationally and communication intensive, but merely support a fixed pre-defined threshold. To address these challenges, we propose an efficient multilevel threshold changeable homomorphic data encapsulation mechanism (MCTh-HDEM). In MCTh-HDEM, we leverage the technique of multilevel threshold changeable secret sharing in order to support both batch encryption and lightweight matrix calculations in the encrypted domain, and also multilevel threshold changeable decryption. Then, we design a lightweight privacy-preserving vehicle positioning scheme (PPVPS), by refining our proposed MCTh-HDEM. The position of the lost and damaged target vehicle on the battlefield would be efficiently inferred by a set of reference vehicles in search unit while protecting positioning related data, and the target vehicle location can be flexibly decrypted by rescue unit. Finally, we give the formal security proofs of our proposed MCTh-HDEM and PPVPS. The performance evaluation and extensive experimental results demonstrate the efficiency and accuracy of our proposal.

Secure task matching has been a crucial research problem in crowdsourcing, requiring the alignment of workers’ preferences and requesters’ task requirements while ensuring user privacy and matching integrity. Recently, some researchers applied blockchain to crowdsourcing, either replacing the platform for decentralization or recording proofs for public verification to defend against malicious platforms. However, they still suffer from unitary coarse-grained matching models or expensive on-chain costs. To address these limitations, we propose PGVMatch, a privacy-aware and fine-grained crowdsourcing task-matching scheme with lightweight on-chain public verifiability. Our scheme is constructed on our newly proposed cryptographic primitive–Multi-authority Attribute-Based Keyword Search with Public Verifiability (MABKS-PV), which avoids access policy leakage and key escrow risks on a single authority, meanwhile adding constant-size proof generation and lightweight verification algorithms to a basic ABKS construction. In PGVMatch, requesters can select workers with fine-grained attribute demands, and workers can pick interested tasks with multi-keyword search, preserving dual-side privacy. The matching process is conducted off-chain, while constant-size proofs are recorded on-chain for efficient and public verification of matching integrity. Security analysis and extensive experiments on the Hyperledger Fabric blockchain demonstrate both the security and our superior performance. PGVMatch outperforms the existing scheme with the fastest matching result verification, achieving a 29% improvement in throughput and a 33% reduction in latency.

With the widespread adoption of electric vehicles (EVs), Vehicle-to-Vehicle (V2V) charging technology offers a more flexible solution for EV charging, significantly alleviating charging inconvenience, particularly in remote areas. However, the current V2V energy trading landscape lacks reliable management platforms with transparent transaction protocols. Furthermore, notable deficiencies exist in data privacy and security, which hinder the broader implementation of V2V charging services. Addressing these challenges, particularly the provision of secure and publicly auditable V2V charging without relying on centralized platforms or disclosing user privacy, has become a critical concern. For this reason, this paper presents PET, a decentralized Privacy-preserving Energy Trading system with public verifiability for EVs. PET is built upon the emerging blockchain technology to decentralize energy trading while ensuring accountability. We model energy demand-supply matching as range matching of locations and charge amounts between a buyer and sellers, and employ reverse auction to select the winner. For efficient and privacy-aware range proofs, we propose a novel batched hash chain-based range proof (BHW) primitive. In addition, PET integrates zk-SNARKs to verify payment correctness while maintaining user privacy. Our system supports public verifiability, entitling any third party to independently verify the transaction integrity. We analyze the privacy guarantees and public verifiability of PET. Extensive experiments implemented on Hyperledger Fabric further validate that PET delivers robust performance with a 4 × reduction in verification cost compared to that without batch proofs.

The Internet of Things (IoT) has greatly changed our lives and generated a large amount of data. Cloud storage helps IoT limited-resource IOT devices process the massive data. However, cloud servers are untrusted in most scenarios as they may illegally obtain sensitive data. Although existing symmetric searchable encryption (SSE) schemes can protect the privacy of outsourced data while preserve data availability, Most of them leak access and search patterns to the cloud server to gain better performance. Such leakages will be used to recover private information. Meanwhile, semi-honestly secure searchable encryption cannot prevent attacks done by the malicious server such as returning the false search result. Therefore, it is still a challenge to prevent malicious cloud server misbehavior, and preserve patterns, simultaneously. This paper proposes OpenSE to solve the aforementioned problems. First, this paper constructs FastOPE as a major building block. With the OPE protocol, the verifiable searchable encryption OpenSE can be trivially realized. After that, security proofs show that OpenSE is secure against malicious cloud servers with access and search pattern hidden. Finally, we implement experiments on real datasets to compare OpenSE with some state-of-the-art works in terms of running time of setup phase and search phase as well as storage overhead. The experimental results show that OpenSE outperforms the state-of-the-art works in terms of setup phase and storage overhead. In addition, the theoretic comparison shows that OpenSE outperforms most existing works in terms of security, in which OpenSE enjoys both verifiability and pattern hidden.

Private set intersection (PSI) facilitates the computation of intersection between the private sets of two parties, ensuring that no additional information beyond the intersection itself is revealed. However, most state-of-the-art are limited to static PSI, leaving updatable PSI untouched. Existing PSI protocols will cost huge computational resources to compute intersection on updated sets. More seriously, none of the existing updatable PSI approaches can achieve both secure addition and deletion operations in once update. To address these challenges, we propose Forward Private Updatable PSI (FUPSI) for two-party setting. FUPSI is designed to support addition and deletion simultaneously, while ensuring forward privacy against semi-honest adversaries. In this work, we analyze the infeasibility of secure synchronous addition and deletion in the existing updatable PSI approaches, by presenting a practical attack which would lead to privacy leakages while deletion function is performed. Then, to resist this attack against semi-honest adversaries, we demonstrate how FUPSI can protect the forward privacy of user sets, by utilizing a variant of keyword Private Information Retrieval (PIR) to hide sensitive intermediate parameters. Specifically in FUPSI, two parties execute keyword PIR to retrieve a flag indicating that the current element is added or deleted so as to determine whether it is in the participants’ datasets. Finally, we provide the formal security proof for our proposed FUPSI, and extensive experimental results demonstrate efficiency and the practicality of our proposal. For instance, the communication complexity of our proposal is only logarithmically related to the size of update sets and the computational overhead is mainly composed of logarithmical times PIR calculations. Owing to the variant of keyword PIR, our work also incurs minimal communication overhead even for enormous datasets, which performs well in updatable settings and slow networks.

Federated learning (FL) allows multiple parties, each holding a dataset, to jointly train a model without leaking any information about their own datasets. In this paper, we focus on vertical FL (VFL). In VFL, each party holds a dataset with the same sample space and different feature spaces. All parties should first agree on the training dataset in the ID alignment phase. However, existing works may leak some information about the training dataset and cause privacy leakage. To address this issue, this paper proposes OpenVFL, a vertical federated learning framework with stronger privacy-preserving. We first propose NCLPSI, a new variant of labeled PSI, in which both parties can invoke this protocol to get the encrypted training dataset without leaking any additional information. After that, both parties train the model over the encrypted training dataset. We also formally analyze the security of OpenVFL. In addition, the experimental results show that OpenVFL achieves the best trade-offs between accuracy, performance, and privacy among the most state-of-the-art works.

Searchable encryption (SE) allows a data owner to outsource encrypted documents to an untrusted cloud server while preserving privacy and achieving secure data sharing. However, most existing SE schemes have a trade-off between security and efficiency. Moreover, these SE schemes leak the server's partial database or search information to perform better. Recent attacks show that such leakages can be used to recover the content of queried keywords or partial database information. To solve this problem and ensure efficiency, this paper proposes labeled searchable encryption (LSE), an efficient searchable encryption scheme based on the labeled private set intersection. We also give formal proofs to prove the security of the proposed labeled PSI protocol and searchable encryption scheme. Finally, we do experiments to compare the performance with some state-of-the-art works, and the experimental results show that the LSE outperforms in terms of total size and generation time of the encrypted database as well as the total search time at client side.

Multiparty private set intersection (PSI) allows several parties, each holding a set of elements, to jointly compute the intersection without leaking any additional information. With the development of cloud computing, delegating the computation to an untrsuted cloud server is becoming a major problem, where the untrusted cloud server may try to get some sensitive information from clients' private information. However, it is complex to build an efficient and reliable scheme to protect user privacy. In order to overcome this problem, we propose DMPSI, an efficient delegated PSI (with cardinality) protocol in a multiparty setting. DMPSI avoids using heavy cryptographic primitives (mainly rely on symmetric-key encryption) to achieve better performance. In addition, both PSI and PSI with the cardinality of DMPSI are secure against semi-honest adversaries and allow any number of colluding clients (at least one honest client). We do experiments to compare the proposed DMPSI with some state-of-the-art works to evaluate overall performance. In addition, we also compare the proposed Oks-PRF with some state-of-the-art multi-point OPRF to highlight our efficiency. The experimental results show that proposed both Oks-PRF and DMPSI(-CA) has better performance and is scalable in the number of clients and the set size.

Since data outsourcing poses privacy concerns with data leakage, searchable symmetric encryption (SSE) has emerged as a powerful solution that enables clients to perform query operations on encrypted data while preserving their privacy. Dynamic SSE schemes have been proposed to handle update operations. However, it is shown that updates might increase the risk of information leakage. Meanwhile, to meet the requirement of real-world applications, it is desirable to have the searchable encryption scheme which supports both multiple clients and multi-keyword queries. To address these issues, this paper proposes MMDSSE, a multi-client forward secure dynamic SSE scheme that supports multi-keyword queries. MMDSSE allows the clients narrow down the results by providing an arbitrary subset of the entire archive, and thus suitable for cloud storage environment. Security analysis and experimental evaluations show that MMDSSE is secure and efficient.

Private Set Intersection (PSI) is one of the most important functions in secure multiparty computation (MPC). PSI protocols have been a practical cryptographic primitive and there are many privacy-preserving applications based on PSI protocols such as computing conversion of advertising and distributed computation. Private Set Intersection Cardinality (PSI-CA) is a useful variant of PSI protocol. PSI and PSI-CA allow several parties, each holding a private set, to jointly compute the intersection and cardinality, respectively without leaking any additional information. Nowadays, most PSI protocols mainly focus on two-party settings, while in multiparty settings, parties are able to share more valuable information and thus more desirable. On the other hand, with the advent of cloud computing, delegating computation to an untrusted server becomes an interesting problem. However, most existing delegated PSI protocols are unable to efficiently scale to multiple clients. In order to solve these problems, this paper proposes MDPPC, an efficient PSI protocol which supports scalable multiparty delegated PSI and PSI-CA operations. Security analysis shows that MDPPC is secure against semi-honest adversaries and it allows any number of colluding clients. For 15 parties with set size of 220on server side and 216on clients side, MDPPC costs only 81 seconds in PSI and 80 seconds in PSI-CA, respectively. The experimental results show that MDPPC has high scalability.

Vehicular social networks (VSNs) are likely to play an increasingly important role of the future smart cities, supporting traffic management, real-time warning, and sharing data (e.g., condition, vehicular, and warning). However, there are existing security and privacy issues with vehicular data, which directly lead to the insufficient utilization of vehicular data. In this article, a blockchain-based lightweight searchable data sharing scheme is presented. Specifically, the proposed protocol achieves efficient data matching and data sharing by uniting the feature of public-key encryption with equality test (PKE-ET) and proxy re-encryption (PRE), meanwhile ensuring reliable matching results by invoking smart contacts. Moreover, a reputation-based dynamic PBFT consensus mechanism is provided. By analyzing the reputation and behavior of the consensus nodes, we further reduce the probability of malicious nodes becoming the consensus nodes, thus decreasing the computing overhead and communication costs in the consensus process. Finally, the security of the proposed protocol is demonstrated based on a computation Diffie–Hellman assumption in the Random Oracle model. According to the performance analysis, it was found that compared to the other protocols, the proposed protocol is computationally lightweight.

Recommender systems facilitate personalized service provision through the statistical analysis and model training of user historical data (e.g., browsing behavior, travel history, etc). To address the underpinning privacy implications associated with such systems, a number of privacy-preserving recommendation approaches have been presented. There are, however, limitations in many of these approaches. For example, approaches that apply public key (fully) homomorphic encryption (FHE) on different users. historical ratings under a unique public key of a target recommendation user incur significant computational overheads on resource-constrained local users and may not be scalable. On the other hand, approaches without utilizing public key FHE can neither resist chosen ciphertext attack (CCA), nor be straightforwardly applied to the setting of distributed servers. In this paper, a lightweight privacy-preserving distributed recommender system is proposed. Specifically, we present a new cryptographic primitive (i.e., tag-based multikey fully homomorphic data encapsulation mechanism; TMFH-DEM) designed to achieve CCA security for both input privacy and result privacy. TMFH-DEM enables a set of distributed servers to collaboratively execute efficient privacy-preserving outsourced computation on multiple inputs encrypted under different secret keys from different data owners, without using public key FHE. Building on TMFH-DEM, we propose a lightweight privacy-preserving distributed recommender system, which flexibly returns all the recommended items with certain predicted ratings for all target users. Formal security proof shows that our proposal achieves both user historical rating data privacy and recommendation result privacy. Findings from our evaluations demonstrate its practicability in terms of scalability, recommendation accuracy, computational and communication efficiency.

Federated learning is increasingly popular, as it allows us to circumvent challenges due to data islands, by training a global model using data from one or more data owners/sources. However, in edge computing, resource-constrained end devices are vulnerable to be compromised and abused to facilitate poisoning attacks. Privacy-preserving is another important property to consider when dealing with sensitive user data on end devices. Most existing approaches only consider either defending against poisoning attacks or supporting privacy, but not both properties simultaneously. In this paper, we propose a differentially private federated learning model against poisoning attacks, designed for edge computing deployment. First, we design a weight-based algorithm to perform anomaly detection on the parameters uploaded by end devices in edge nodes, which improves detection rate using only small-size validation datasets and minimizes the communication cost. Then, differential privacy technology is leveraged to protect the privacy of both data and model in an edge computing setting. We also evaluate and compare the detection performance in the presence of random and customized malicious end devices with the state-of-the-art, in terms of attack resiliency, communication and computation costs. Experimental results demonstrate that our scheme can achieve an optimal tradeoff between security, efficiency and accuracy.

Proxy Re-Encryption (PRE), a special cryptographic primitive, can efficiently perform ciphertext conversion on the cloud. To enable the data owner (i.e. delegator) to authorize a file access path according to the different priorities of the users (i.e. delegatees), autonomous path proxy re-encryption (AP-PRE) was proposed, where the delegator can generate a proxy re-encryption autonomous path in order of the delegatees’ priority. If one delegatee does not hold the decryption right, the ciphertext can be converted to a new ciphertext that can be decrypted by the next delegatee with lower priority in the path. Although AP-PRE enables the delegator to pre-define the whole decryption path, the access policy only supports a linked path and the data owner disallows the nodes in the proxy path to generate delegating branches to access its data. Such a linked path may be too long in practice, especially when the system scales up (i.e. the average complexity of encrypted data access isO(n)wherendenotes the number of delegatees). Hence, we propose a generalized autonomous path proxy re-encryption (APB-PRE) scheme for supporting branch functionality. Firstly, by setting the token and a carefully designed ciphertext structure, the branch functionality of the path delegation is realized. Specifically, we utilize the bilinearity of bilinear pairing to construct the token for the transition of the label embedded in the ciphertext in different paths, resulting in a far more flexible access structure with a tree-like topology. In APB-PRE, the delegatees with lower priority who need to share data can complete the decryption task earlier, without affecting the decryption of the high-priority delegatees. Finally, we prove that it achieves IND-HRA security under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Benefiting from the creation of branching paths, users on the branching path can get the re-encrypted ciphertext much earlier. Therefore, the average complexity of encrypted data access reduces toO(log n)compared to AP-PRE. The experimental results show that our proposal can extend the branching functionalities of AP-PRE with only moderate computational cost.

Real-time navigation is a fundamental service with the emerging techniques of intelligent transportation and crowdsensing. Unfortunately, the breach of location privacy significantly impedes its wide adoption. Most existing state-of-the-art exploit either pseudonyms or public key fully homomorphic encryption (FHE) to protect location privacy, which requires an online certificate authority (CA) or loads intolerably heavy computational/communication overhead on resource-constrained vehicular users. In this paper, a new cryptographic primitive named efficient multiparty delegated computation (MPDC) is firstly proposed, where any one-way trapdoor permutation is required to perform constant times (i.e. twice) on each resource-constrained data provider, to encrypt a batch of messages and enable types of secure evaluations over ciphertexts encrypted under multiple keys of multiple parties. Based on MPDC, we devise a lightweight privacy-preserving real-time intelligent traffic navigation scheme (EPNS) in cloud-assisted VANETs. The proposed approach predicts an optimal driving route of shortest time without disclosing either vehicular users' private location or the navigation result to the collusion between the semi-trusted cloud/cryptography service provider (CSP) and unauthorized users, by securely evaluating auto-regression moving average (ARMA) model with spatiotemporal correlations of high accuracy and efficiency. Finally, formal security proofs and the extensive evaluations demonstrate the utility and the practicability of our approaches.

Recent methods in sequential recommendation focus on learning multi-interest embedding vectors from a user's behavior sequence for the next-item recommendation. However, behavior sequential data may result from users' conformity towards popular items, which entangles users' real interests and tends to recommend popular items by using interest embeddings. In this paper, we propose a novel multi-interest framework with causal embedding for sequential recommendation, called MiceRec. Specifically, we first obtain two embedding layers from behavior sequence by assigning items with separate embeddings for interest and conformity, then extract multiple pure interests from one embedding layer, while the other for users' conformity extraction. According to the colliding effect of causal inference, we mine cause-specific data for training causal embeddings. Our framework significantly outperforms state-of-the-art solutions on two real-world datasets1. We further demonstrate that the learned multi-interest embeddings successfully separate from each other, and show that conformity information is almost squeezed out from interest embeddings.

Smart grids bring advantages of reliability and high efficiency by real-time communication technologies in contrast to the traditional grids. However, smart grids also raise concerns about privacy and security for the individual fine-grained information collection. In order to guarantee privacy and security in the grids, we propose an efficient and privacy-preserving multidimensional data aggregation scheme without a third trusted party and supporting fast error detection, named EPMDA-FED, in the article. First, we adopt a Chinese Remainder Theorem (CRT) to pack multidimensional data and encrypt the processed data using the keys generated by the negotiation among users and the control center (CC). Second, our scheme is efficient for encryption without high-cost additive homomorphic public-key encryption (PKE) scheme, such as the Paillier cryptosystem and supporting batch verification with fast error detection. Our proposed error detection algorithm is quite efficient with sublinear computational complexity. Besides, through security analysis, EPMDA-FED is semantically secure against collusion attack and the consistency of negotiated key, authenticity, and data integrity of the users’ reports are guaranteed. Finally, performance evaluation shows EPMDA-FED is more efficient than the existing competing approaches in terms of computational and communication overheads.

Signal processing in the encrypted domain is a potential tool to protect sensitive signals against untrusted cloud servers and unauthorized users in the delegated computing setting, without affecting the accuracy of large volume of signal analyzing and processing. Most existing approaches use Paillier’s public key additively homomorphic encryption to encrypt each signal in a large bundle; thus, incurring significant computational costs at local, often resource-constrained, devices while guaranteeing only signal input privacy. To address these limitations, in this paper, an efficient privacy-preserving outsourced discrete wavelet transform scheme (PPDWT), comprising PPDWT-1 and PPDWT-2, without leveraging public key (fully) homomorphic encryption is proposed. Specifically, PPDWT-1 is proposed to achieve signal input privacy against the collusion between the honest-but-curious cloud and unauthorized users, and the proposed PPDWT-2 protects both signal input privacy and coefficient privacy against collusion attacks. Both constructions leverage the offline execution of any one-way trapdoor permutation only once to encrypt batch signals, and permit signal processing in the encrypted domain. In our approach, only authorized users can successfully decipher the result of discrete wavelet transform. Compared to the$O(|l|)$computational complexity on the user’s end in existing state-of-the-art public key homomorphic encryption-based techniques, our approach only incurs$O(1)$computational complexity which is independent to size of the signal inputs$|l|$. We also discuss the expanding factor, the upper bound and various extensions to privacy-preserving discrete cosine/fourier transform in the encrypted domain. Finally, our proposed PPDWT is formally proved secure under the universal composability (UC) model. We then evaluate the proposed approach using case studies to demonstrate its effectiveness and practicability.

Self-organizing vehicular social networks underpin many location-based services (LBS) such as those that collect and share environmental information (e.g., traffic and weather conditions) among vehicular users and the infrastructure. There are, however, security and privacy considerations in the sharing of such information, and one popular approach is to design lightweight authentication solutions for LBS. Existing approaches may suffer from limitations such as significant computational and/or storage overheads, latency and time delays, and consequently impractical for resource-constrained on-board units. In this paper, we propose an efficient privacy-preserving LBS bundle authentication scheme (hereafter referred to as SAVE) through secure redundancy filtering in self-organizing vehicular social networks. Firstly, an enhanced self-healing key distribution protocol with distributed revocation is proposed to reduce communication cost for retransmitting lost key material and resist free-riding attacks to enhance the authentication efficiency. Then, based on it, a generalized version of online/offline aggregate signature is proposed to achieve batch LBS bundle verification based on arbitrary one-way function holding the property of multiplicative homomorphism. Finally, an efficient zero-knowledge range proof based on lightweight one-way hash chain is designed to decide the redundancy of LBS bundles without disclosing vehicular users’ location privacy. Formal security proof and extensive simulation results demonstrate that our proposed SAVE achieves identity privacy, two levels of location privacy and the practicability in reality.

Parking has been a big problem in metro cities. On one hand, it is very difficult to find a public parking lot in convenience. On the other hand, a lot of private parking lots are at waste while their owners are at work. Parking lots sharing emerged to address this issue. It allows more parking choices on the drivers side, while brings economic benefits on the parking lots side. In this paper, a privacy-preserving scope-query searchable encryption scheme based on blockchain (PSSBP) is proposed for parking lots sharing in vehicular networks. PSSBP manipulates dynamic group signatures, Cuckoo filter, Pedersen commitment and smart contract to achieve privacy-preservation and fairness. The security analysis and experimental results show that PSSBP is feasible for parking lots sharing in real life environments.

Bitcoin, the first decentralized cryptocurrency system, uses a simple but effective difficulty adjustment algorithm to stabilize its average time of the block creation at 10 minutes. Over time, the volatility of the Bitcoin price has become higher and higher, and it causes the total hashrate (the hash power of the entire network) constantly fluctuating. Both facts and our experimental results prove that Bitcoin's difficulty adjustment algorithm cannot respond in time while the total hashrate is constantly fluctuating. Hence, we propose a consensus protocol with a real-time difficulty adjustment algorithm, RTPoW. RTPoW allows the blockchain to adjust the difficulty target of each block by predicting the real-time total hashrate, so the block time can remain stable even if the total hashrate is wildly fluctuating. To evaluate the effect of RTPoW, we implemented a simulator of an experimental environment and tested our algorithm. The results obtained have confirmed its effectiveness and stability.

In this paper, we introduce DPFDT - a decentralized privacy-preserving fair data trading system using smart contracts. In this system, data as a digital commodity can be sold by the seller to the buyer at a certain price. We first design a conditional anonymous scheme based on block-chain and a fair trading protocol. Then we integrate them to form DPFDT to provide fairness, conditional anonymity, privacy-preserving, and decentralization. A conditional anonymous scheme means that the user’s operations are carried out through anonymous accounts, but when an anonymous account does some malicious behaviors, its real account will be traced. A protocol is said to be fair if and only if the buyer pays, and he will be guaranteed to receive the correct data. While a few fair exchange protocols based on smart contracts have been proposed, the DPFDT has the following advantages: (1) Adding privacy-preserving into the system, including the privacy of both parties’ identities in trading and the privacy of the buyers’ needs; (2) Authorizing buyers the right to specify the order. That is, sellers will be asked in the order specified by the buyer; (3) Introducing a paid trial stage in the trading process, the loss of paying for unwanted goods will be minimized. Besides, security analysis shows that the proposed scheme has transaction security and trade security. Finally, experiments are done on the Ethernet platform. It shows that the DPFDT is efficient and practical in real-life applications.

Abstract Searchable encryption (SE) allows the cloud server to search over the encrypted data and leak information as little as possible. Most existing efficient SE schemes assume that the leakage of search pattern and access pattern is acceptable. A series of work was proposed, instructing malicious users to use this leakage to come up with attacks. Especially, with a devastating attack proposed by Zhang et al., the cloud server can reveal the keywords queried by normal users by using some injected files. From the method of constructing uniform $(k,n)$-set of a finite set $A$ proposed by Cao, we put forward a new file-injection attack. In our attack, the server needs fewer injected files than the previous attack when the size of $T$ is larger than 9 and the size of keyword set is larger than $2T$, where $T$ is the threshold of the number of keywords in each injected file. Our attack is more practical and easier to implement in the real scenario.

With the fast development of Internet of Things (IoT) especially for smart grid and electric vehicle (EV) networking, vehicle-to-grid (V2G) communications have been increasingly studied and recognized as one of the most convincing tools for general road transportation, to effectively reduce the oil demands and gas emissions. Unfortunately, a series of security and privacy issues have significantly impeded its wide adoption. The existing work mainly focused on the static environment, which cannot be directly applied to the mobile setting where EVs travel across regions. The dynamic pricing metric in V2G networks depends on the real-time electricity usage aggregation in one region. To address this issue, in this article, an efficient privacy-preserving data aggregation and dynamic pricing service PADP in V2G IoT is proposed, by designing an identity-based sequential aggregate signed data (SASD) based on factoring and a threshold homomorphic encryption. In the proposed threshold homomorphic encryption, a legal ciphertext can be generated if and only if no less than threshold k individual illegal ciphertexts are aggregated. Therefore, the aggregated power consumption data can be successfully decrypted while the individual power consumption privacy of honest EV users can be well protected against even the collusion between a malicious power charging station and compromised EVs. Furthermore, the technique of SASD guarantees entity authentication with a minimized amount of transmitted data. Finally, formal security proof and extensive performance evaluation demonstrate the effectiveness and practicability of our proposed PADP.

The demands for the fairness, security, and efficiency of the supply chain have grown significantly due to the rise of globalization. However, some problems of the information flow, logistics, and capital flow in the supply chain remain a challenge, such as the information asymmetry between upstream and downstream, substandard quality of goods, difficulty in traceability, and default of payment. Therefore, this paper proposes a blockchain-based supply chain framework (SESCF), which solves the supply chain problems securely and efficiently. First, the use of blockchain and smart contracts ensures the information symmetry in the supply chain system. Second, the radio frequency identification (RFID) provides a unique identity of goods, which helps in real-time quality monitoring. Additionally, the immutability and distributed storage of the blockchain play an important role in tracking the origin of goods. Third, the efficient payment channel is used to solve the problem of payment defaults. Furthermore, simulations of smart contracts along with the security analyses are presented in this paper. We also implement a blockchain-based supply chain system (SescfDapp), which is built upon a Consortium blockchain. Large-scale experiments and detailed analysis prove the feasibility and efficiency of our proposed system.

Outsourced pattern matching delegates the task of finding all positions pattern$P$appears in text$T$to the cloud from resource-constrained devices. Unfortunately, it has brought a series of security and privacy issues. Most of the state-of-the-art either disclosed text/pattern privacy or exploited the computationally-intensive techniques of public key fully homomorphic encryption (FHE), commitment schemes and zero knowledge proof to achieve both text/pattern privacy and verifiability. To address these issues, as a building block, an efficient privacy preserving verifiable outsourced discrete fourier transform protocol OVFT is first devised based on any one-way trapdoor permutation (OWTP). Based on OVFT, we propose an efficient secure verifiable outsourced polynomial multiplication protocol OPVML which is further exploited in designing our final protocol PVOPM for verifiable privacy-preserving outsourced pattern matching. Without exploiting public key FHE, the proposed PVOPM achieves both verifiability and text/pattern privacy against the collusion between the cloud and malicious receiver/sender, by generating authentication proofs of constant size and executing constant times of any one-way trapdoor permutation, independent to both the text size$n$and the pattern size$m$. Finally, formal security proof under universal composable (UC) model and extensive evaluations demonstrate the efficiency and practicability of our proposed PVOPM.

Secure cloud storage, which is an emerging cloud service, is designed to protect the confidentiality of outsourced data but also to provide flexible data access for cloud users whose data is out of physical control. Ciphertext-Policy Attribute-Based Encryption (CP-ABE) is regarded as one of the most promising techniques that may be leveraged to secure the guarantee of the service. However, the use of CP-ABE may yield an inevitable security breach which is known as the misuse of access credential (i.e., decryption rights), due to the intrinsic “all-or-nothing” decryption feature of CP-ABE. In this paper, we investigate the two main cases of access credential misuse: one is on the semi-trusted authority side, and the other is on the side of cloud user. To mitigate the misuse, we propose the first accountable authority and revocable CP-ABE based cloud storage system with white-box traceability and auditing, referred to as CryptCloud±. We also present the security analysis and further demonstrate the utility of our system via experiments.

Searchable Symmetric Encryption(SSE) remains to be one of the hot topics in the field of cloud storage technology. However, malicious servers may return incorrect search results intentionally, which will bring significant security risks to users. Therefore, verifiable searchable encryption emerged. In the meantime, single-keyword query limits the applications of searchable encryption. Accordingly, more expressive searchable encryption schemes are desirable. In this paper, we propose a verifiable conjunctive keyword search scheme based on Cuckoo filter (VCKSCF), which significantly reduces verification and storage overhead. Security analysis indicates that the proposed scheme achieves security in the face of indistinguishability under chosen keyword attack and the unforgeability of proofs and search tokens. Meanwhile, the experimental evaluation demonstrates that it achieves preferable performance in real-world settings.

Machine learning, particularly the neural network (NN), is extensively exploited in dizzying applications. In order to reduce the burden of computing for resource-constrained clients, a large number of historical private datasets are required to be outsourced to the semi-trusted or malicious cloud for model training and evaluation. To achieve privacy preservation, most of the existing work either exploited the technique of public key fully homomorphic encryption (FHE) resulting in considerable computational cost and ciphertext expansion, or secure multiparty computation (SMC) requiring multiple rounds of interactions between user and cloud. To address these issues, in this article, a lightweight privacy-preserving model training and evaluation scheme LPTE for discretized NNs (DiNNs) is proposed. First, we put forward an efficient single key fully homomorphic data encapsulation mechanism (SFH-DEM) without exploiting public key FHE. Based on SFH-DEM, a series of atomic calculations over the encrypted domain, including multivariate polynomial, nonlinear activation function, gradient function, and maximum operations are devised as building blocks. Furthermore, a lightweight privacy-preserving model training and evaluation scheme LPTE for DiNNs is proposed, which can also be extended to convolutional NN. Finally, we give the formal security proofs for dataset privacy, model training privacy, and model evaluation privacy under the semi-honest environment and implement the experiment on real dataset MNIST for recognizing handwritten numbers in DiNN to demonstrate the high efficiency and accuracy of our proposed LPTE.

Blockchain can be utilized to enhance both security and efficiency for location-based service (LBS) in vehicular social networks (VSNs), due to its inherent decentralization, anonymity, and trust properties. Unfortunately, the existing approaches either lack effective authentication, which is vulnerable to the man-in-the-middle attack, or require an online certificate authority (CA) where frequent interactions with resource-constrained vehicles are required. To address these challenging issues, in this article, a lightweight threshold CA for consortium blockchain along with a privacy-preserving LBS protocol in blockchain enforced VSNs is proposed. First, a lightweight threshold CA framework LTCA is proposed by devising a threshold proxy signature, where the proxy signing key is issued by a coalition of threshold number of CAs playing the roles of authorized nodes in the consortium blockchain. Without the intervene of an online CA, each vehicle in the online phase can authenticate its identity by itself each time its blockchain address (i.e., account address) is updated. Then, based on the proposed LTCA, an efficient privacy-preserving LBS protocol PPVC is contrived to protect each vehicle’s conditional identity privacy with a moderate cost. Finally, both security analysis and performance evaluation demonstrate the effectiveness and efficiency of our proposed LTCA and PPVC in blockchain enforced VSNs.

Location-based service (LBS) in vehicular ad hoc networks (VANETs) has significantly benefited information acquisition from geographically-based social networking. Authentication guarantees the unforgeability and the effectiveness of the LBS information. Unfortunately, owing to a large quantity of redundant or useless LBS messages disseminated in VANETs, the heavy authentication overhead of the existing work adopting a periodically released authentication key, filtering with message identifiers or exploiting public key (fully) homomorphic encryption (FHE), is either intolerable by resource-constrained on-board units (OBUs) or inappropriate to the realtime controlling requirement for VANETs. In this paper, an efficient multi-key secure outsourced computation scheme MSOC without exploiting public key FHE is first proposed, in the setting of two non-colluding servers, namely the cloud and the cryptographic service provider (CSP). Then, based on MSOC, an efficient and secure comparison protocol LSCP is devised, without the interaction between the server and the users. Furthermore, a lightweight privacy-preserving authentication protocol LPPA for LBS in VANETs is proposed, by eliminating duplicate and useless encrypted LBS messages before authentication is executed, through a newly devised efficient privacy-preserving information filtering system. Both user’s location privacy and interest privacy are well protected against even the collusion between the roadside units (RSUs) serving as the cloud (or CSP) and malicious users. Especially, the property of ciphertext re-encryption of our proposed MSOC also guarantees the interest pattern privacy whether two users accept the same LBS information. Finally, formal security proof and extensive simulation results verify the effectiveness and practicability of our proposed LPPA.

ABKS has drawn much attention from research and industry in recent years, an ABKS scheme is an encryption scheme that supports keyword search and access control. Attribute-Based Encryption is a public key encryption that enables users to encrypt and decrypt message based on attributes. In a typical implementation, the size of the ciphertext is proportional to the number of attributes associated with it and the decryption time is proportional to the number of attributes used during decryption. Inherit from ABE technology, the computation cost and ciphertext size in most ABKS schemes grow with the complexity of the access policy. On the other hand, we found that the traditional ABKS schemes cannot resist our secret-key-recovery attack. To deal with the above problems, we present new ciphertext policy attribute based encryption with fast keyword search constructions. Our constructions preserve the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. Our constructions feature multi-value-independent compared with the existing attribute based searchable encryption schemes. The performance analysis demonstrates the efficiency of our constructions. We offer rigorous security proof of our second scheme, which is IND-CKA and IND-CPA secure.

Cognitive radio network (CRN) enables dynamic spectrum management where spectrum aggregation and sharing have significantly facilitated relieving the supply-demand gap among skyrocketing number of mobile devices. Secondary users (SUs) can collaboratively detect and exploit the available spectrums of primary users (PUs) from different locations. Unfortunately, it has brought a series of security threats leaking user's location privacy to unauthorized entities. The existing work either had privacy breaches or depended on computationally-intensive public key homomorphic encryption loading intolerably high complexity on resource-constrained SUs. To well address these issues, in this paper, a lightweight privacy-preserving spectrum aggregation and auction scheme PPSAS is proposed in CRNs without leveraging public key homomorphic encryption. Firstly, a new primitive of efficient privacy preserving multiparty data aggregation protocol PPMDA is proposed based on any one-way trapdoor permutation. Then based on PPMDA, an efficient privacy preserving spectrum aggregation scheme PPSRA and spectrum auction scheme PPSSA are respectively devised to constitute our proposed PPSAS. Especially, a decentralized PPMDA is extended to resist collusion attack and a secure extended outsourced stable complete marriage matching protocol is given to flexibly realize SU/auctioneer optimization. Finally, formal security proof and extensive simulations demonstrate that our proposed PPSAS well protects the SUs' location privacy against collusion attacks and possesses the advantages over the sate-of-the-art in terms of auctioneer's revenue, SUs' satisfaction, computational and communication overhead.

Searchable encryption is a cryptographic primitive that allows users to search for keywords on encrypted data. It allows users to search in archives stored on cloud servers. Among searchable encryption schemes, those supporting multiuser settings are more suitable for daily application scenarios and more practical. However, since the cloud server is semi-trusted, the result set returned by the server is undefined, and most existing multi-user searchable encryption schemes rely heavily on trusted third parties to manage user permission. To address these problems, verifiable multi-user searchable encryption schemes with dynamic management of user search permissions, weak trust on trusted third parties and are desirable. In this paper, we propose such a scheme. Our scheme manages user permission and key distribution without a trusted third party. User search permission and user access permission matrices are generated separately to manage user permissions dynamically. In addition, our scheme can verify the result set returned by the cloud server. We also show that our scheme is index and trapdoor indistinguishable under chosen keyword attacks in the random oracle model. Finally, a detailed comparison experiment is made by using the actual document data set, and the results show that our scheme is efficient and practical.

Abstract Lossy trapdoor functions (LTDFs) were introduced by Peikert and Waters (STOC 2008) and have a number of applications in cryptography. All-but-many lossy trapdoor functions (ABM-LTDFs) are generalizations of LTDFs studied by Hofheinz (Eurocrypt 2012). Specially, using ABM-LTDFs to construct public key encryption (PKE) scheme with selective opening security has been proven feasible. Existing ABM-LTDFs were built on pairings, lattices and decisional composite residuosity (DCR) assumption. However, pairing-based ABM-LTDFs and DCR-based ABM-LTDFs rely on non-standard assumptions. In this paper, we construct an ABM-LTDF under the decisional RSA subgroup (DRSA) assumption, and we employ it to construct PKE scheme with selective opening security. We also propose a construction of DCR-based ABM-LTDF relying on standard assumption in Appendix.

Fault-tolerant aggregate signatures allow the verification algorithm to recognize and verify all the valid individual signatures in an aggregate signature. However, in ordinary aggregate signature schemes, if there is a single faulty individual signature in the valid aggregate, the whole aggregate will be invalid. This will make great difficulties in many applications including secure logging and batch verification in vehicular ad hoc network et al. In this paper, inspired by the finite set theory called uniform (k,n)-set proposed by Cao, we put forward a novel fault-tolerant aggregate signature scheme. Our new scheme is more efficient compared with previous fault-tolerant aggregate signature scheme based on cover-free family. It is noted that our scheme is quite easy to be implemented in the real scenario.

While designing ring signature and group signature is a relatively mature area, few published schemes are both efficient and quantum attack-resilience. In this paper, we present two new signature schemes based on coding theory. First, we present two new zero-knowledge (ZK) identification protocols based on the construction of (Cayrel, P.L., Véron, P. and Alaoui, S.M.E.Y. (2010) A Zero-Knowledge Identification Scheme Based on the q-ary Syndrome Decoding Problem. Proceedings of SAC 2010, Waterloo, Ontario, Canada, August 12–13, pp. 171–186. Springer, Berlin) in order to improve efficiency of code-based digital signature schemes. We then transform the newly proposed ZK protocols into a ring signature scheme and a group signature scheme. Our schemes enjoy a significant improvement in efficiency since reducing the cheating probability decreases the interaction rounds. Specially, with the security level of 2−87, the sizes of public key and signature are 14.5 KB and 52 KB in our ring signature scheme, while the corresponding sizes are 400 KB and 2384 KB in the scheme of (Cayrel, P. L., Alaoui, S. M. E. Y., Hoffmann, G. and Véron, P. (2012) An improved threshold ring signature scheme based on error correcting codes. Proceedings of WAIFI 2012, Bochum, Germany, July 16–19, pp. 45–63. Springer, Berlin). At the security level of 2−80, the sizes of public key and signature are 32 KB and 113.8 KB in our group signature scheme, as compared with 2.5 MB and 20 MB in the scheme of (Alamélou, Q., Blazy, O., Cauchie, S. and Gaborit, P. (2017) A codebased group signature scheme. Des. Codes Cryptogr., 82, 469–493) and 642 KB and 114 KB in the scheme of (Ezerman, M.F., Lee, H.T., Ling, S., Nguyen, K. and Wang, H. (2015) A provably secure group signature scheme from code-based assumptions. Proc. ASIACRYPT 2015, Auckland, New Zealand, November 29–December 3, pp. 260–285. Springer, Berlin).

Attribute-based encryption with keyword search (ABKS) is a system that supports keyword search and access control. By inheriting from attribute-based encryption technology, most current ABKS schemes incur large computation costs in encryption phase and keyword search phase. In a typical implementation, the size of ciphertext is proportional to the number of attributes associated with the access policy, so that the keyword search time and the decryption time are proportional to the number of attributes used in the access policy. To deal with the above problem, we present a new ciphertext-policy attribute-based encryption with fast keyword search scheme. Our scheme preserves the fine-grained access control inherited from the ABE system while supporting hidden policy and fast keyword search. In particular, the proposed scheme can efficiently support AND-gate access policy with multiple attribute values. Moreover, our scheme features multi-value-independent which have constant computation costs compared with the existing ABKS schemes. With the “aggregation” technique, the keyword search phase only needs three pairings, which is a great advantage over previous ABKS schemes. We offer rigorous security proof of our scheme, and the performance analysis demonstrates the efficiency of our scheme.

For disaster recovery, we store backups of a file in several positions far away from each other. If we delegate this task to an untrusted cloud service provider, a verification method is desired. In this paper, we construct a generic transformation from “proof of retrievability” to “proof of multic opy”. We present this work in two stages. In the first stage, we propose a generic protocol of “proof of file position” based on an arbitrary secure “proof of retrievability” protocol. In the second stage, we propose a “proof of multicopy” protocol based on our “proof of file position” protocol. Both of our protocols are provably secure.

Ciphertext-policy attribute-based encryption (CP-ABE) has been proposed to enable fine-grained access control on encrypted data for cloud storage service. In the context of CP-ABE, since the decryption privilege is shared by multiple users who have the same attributes, it is difficult to identify the original key owner when given an exposed key. This leaves the malicious cloud users a chance to leak their access credentials to outsourced data in clouds for profits without the risk of being caught, which severely damages data security. To address this problem, we add the property of traceability to the conventional CP-ABE. To catch people leaking their access credentials to outsourced data in clouds for profits effectively, in this paper, we first propose two kinds of non-interactive commitments for traitor tracing. Then we present a fully secure traceable CP-ABE system for cloud storage service from the proposed commitment. Our proposed commitments for traitor tracing may be of independent interest, as they are both pairing-friendly and homomorphic. We also provide extensive experimental results to confirm the feasibility and efficiency of the proposed solution.

As a sophisticated mechanism for secure finegrained access control over encrypted data, ciphertext-policy attribute-based encryption (CP-ABE) is one of the highly promising candidates for cloud computing applications. However, there exist two main long-lasting open problems of CP-ABE that may limit its wide deployment in commercial applications. One is that decryption yields expensive pairing cost which often grows with the increase of access policy size. The other is that one is granted access privilege for unlimited times as long as his attribute set satisfies the access policy of a given ciphertext. Such powerful access rights, which are provided by CP-ABE, may be undesirable in real-world applications (e.g., pay-as-youuse). To address the above drawbacks, in this paper, we propose a new notion called auditable σ-time outsourced CF-ABE, which is believed to be applicable to cloud computing. In our notion, expensive pairing operation incurred by decryption is offloaded to cloud and meanwhile, the correctness of the operation can be audited efficiently. Moreover, the notion provides σ-time fine-grained access control. The cloud service provider may limit a particular set of users to enjoy access privilege for at most σ times within a specified period. As of independent interest, the notion also captures key-leakage resistance. The leakage of a user's decryption key does not help a malicious third party in decrypting the ciphertexts belonging to the user. We design a concrete construction (satisfying our notion) in the key encapsulation mechanism setting based on Rouselakis and Waters (prime order) CP-ABE, and further present security and extensive experimental analysis to highlight the scalability and efficiency of our construction.

Wireless sensor networks (WSNs) are increasingly used in smart cities which involve multiple city services having quality of service (QoS) requirements. When misbehaving devices exist, the performance of current delivery protocols degrades significantly. Nonetheless, the majority of existing schemes either ignore the faulty behaviors’ variability and time-variance in city environments or focus on homogeneous traffic for traditional data services (simple text messages) rather than city services (health care units, traffic monitors, and video surveillance). We consider the problem of fault-aware multiservice delivery, in which the network performs secure routing and rate control in terms of fault activity dynamic metric. To this end, we first design a distributed framework to estimate the fault activity information based on the effects of nondeterministic faulty behaviors and to incorporate these estimates into the service delivery. Then we present a fault activity geographic opportunistic routing (FAGOR) algorithm addressing a wide range of misbehaviors. We develop a leaky-hop model and design a fault activity rate-control algorithm for heterogeneous traffic to allocate resources, while guaranteeing utility fairness among multiple city services. Finally, we demonstrate the significant performance of our scheme in routing performance, effective utility, and utility fairness in the presence of misbehaving sensors through extensive simulations.

The paper presents an identity based encryption (IBE) under selective opening attacks (SOA) whose security is almost-tightly related to a set of computational assumptions in composite-order bilinear groups. Our result is a combination of Bellare, Waters and Yilek's method [TCC, 2011] for constructing (not tightly) SOA secure IBE and Hofheinz, Koch and Striecks’ technique [PKC, 2015] on building almost-tightly secure IBE in the multi-ciphertext setting. In the paper, we first tune Bellare et al.’s generic construction for SOA secure IBE to show that a one-bit IBE achieving ciphertext indistinguishability under chosen plaintext attack in the multi-ciphertext setting (with one-sided public openability) tightly implies a multi-bit IBE secure under the selective opening attack. Next, we almost tightly reduce such a one-bit IBE to static assumptions in the composite-order bilinear groups employing the technique of Hofheinz et al. This yields the first SOA secure IBE with almost-tight reduction.

Summary Smart grid is ever‐increasingly adopted by nations and governments for its grand development with the underlying technologies of power management and information communications. The real‐time electricity usage can be monitored and aggregated by the smart meters deployed in the household for further analysis, control, and pricing. However, the existing solutions mainly depend on Paillier's additive homomorphic encryptions taking high computational complexity, and the aggregation mainly focused on the time series data generated by one single user. In this paper, we propose an efficient privacy‐preserving temporal and spacial data aggregation from one‐way functions in smart grid communications, which also allows spacial data aggregation from multiple users. The proposed construction can guarantee the unconditional security of users' metering power data privacy from the community gateway and the operation center, and the Adaptive Chosen Ciphertext Attack (CCA2) security of the aggregation result that can only be accessed by the authorized operating center. Both temporal and spacial data aggregation only require computing the underlying one‐way function once. The formal security proof and performance evaluations demonstrate the efficiency and the practicability of our scheme. Copyright © 2015 John Wiley & Sons, Ltd.

Abstract Enabling privacy preserving outsourced data aggregation is regarded as an important issue for multi‐domain wireless networks. In this paper, we present a novel hybrid cloud‐based privacy‐preserving outsourced data aggregation framework. To achieve this, we introduce a hybrid storage cloud and aggregator cloud architecture, in which both of the storage clouds and aggregator cloud are assumed to be untrusted. On the basis of this security assumption, we firstly propose two novel basic protocols, including the proactive privacy‐preserving aggregation and reactive privacy‐preserving aggregation schemes, which are based on the idea of secret sharing. The proactive scheme allows the user to proactively split their data to multiple storage clouds to avoid data leaking while the reactive scheme allows the users to store their encrypted data in storage cloud and aggregator to finish the data aggregation based on the encrypted data. Moreover, on the basis of the proactive privacy‐preserving data aggregation and reactive privacy‐preserving data aggregation, we further propose an advanced protocol, which can resist the malicious data mining attack. The detailed performance simulations are given to demonstrate the security, effectiveness, and efficiency of the proposed scheme. Copyright © 2014 John Wiley & Sons, Ltd.

Nowaday, secure cloud computation has become more and more attractive. We propose a protocol for privacy-preserving pattern matching. Then, we prove that the construction is simulation-based secure in the semi-honest setting. Finally, the efficiency analysis shows our scheme outperforms the previous works. Specially, compared with Fast' protocol our scheme is more efficient and the text owner does not need real-time online in the query phase. Thus, this protocol is flexible to query by light weight equipments such as mobile phones. Copyright © 2016 John Wiley & Sons, Ltd.

Verifiable outsourced computation enables a computational resource-constrained mobile device to outsource the computation of a function F on multiple inputs x1, …, xnto the cloud that is generally assumed to possess abundant powers. The most existing work depends on Yao's Garbled Circuit and fully homomorphic encryptions that took considerable computational overhead on weak clients. In this paper, a more efficient verifiable outsourced computation of encrypted data EVOC supporting any functions from any one-way trapdoor function is proposed, based on our newly-devised privacy preserving data aggregation supporting both addition and multiplication operations without exploiting fully homomorphic encryption (FHE). It solves the open problem suggested by Gennaro et al. that how to devise a verifiable computation scheme that used a more efficient primitive than FHE. Finally, the formal security proof and extensive efficiency evaluations demonstrate our proposed EVOC satisfies the target security and privacy requirements and far outperforms the state-of-the-art in terms of computational and communication complexity.

Cloud-assisted e-healthcare systems significantly facilitate the patients to outsource their personal health information (PHI) for medical treatment of high quality and efficiency. Unfortunately, a series of unaddressed security and privacy issues dramatically impede its practicability and popularity. In e-healthcare systems, it is expected that only the primary physicians responsible for the patients treatment can not only access the PHI content but verify the real identity of the patient. Secondary physicians participating in medical consultation and/or research tasks, however, are only permitted to view or use the content of the protected PHI, while unauthorized entities cannot obtain anything. Existing work mainly focuses on patients conditional identity privacy by exploiting group signatures, which are very computationally costly. In this paper, we propose a white-box traceable and revocable multi-authority attribute-based encryption named TR-MABE to efficiently achieve multilevel privacy preservation without introducing additional special signatures. It can efficiently prevent secondary physicians from knowing the patients identity. Also, it can efficiently track the physicians who leak secret keys used to protect patients identity and PHI. Finally, formal security proof and extensive simulations demonstrate the effectiveness and practicability of our proposed TR-MABE in e-healthcare cloud computing systems.

Ciphertext-policy attribute-based encryption (CP-ABE) enables fine-grained access control to the encrypted data for commercial applications. There has been significant progress in CP-ABE over the recent years because of two properties called traceability and large universe, greatly enriching the commercial applications of CP-ABE. Traceability is the ability of ABE to trace the malicious users or traitors who intentionally leak the partial or modified decryption keys for profits. Nevertheless, due to the nature of CP-ABE, it is difficult to identify the original key owner from an exposed key since the decryption privilege is shared by multiple users who have the same attributes. On the other hand, the property of large universe in ABE enlarges the practical applications by supporting flexible number of attributes. Several systems have been proposed to obtain either of the above properties. However, none of them achieve the two properties simultaneously in practice, which limits the commercial applications of CP-ABE to a certain extent. In this paper, we propose two practical large universe CP-ABE systems supporting white-box traceability. Compared with existing systems, both the two proposed systems have two advantages: 1) the number of attributes is not polynomially bounded and 2) malicious users who leak their decryption keys could be traced. Moreover, another remarkable advantage of the second proposed system is that the storage overhead for traitor tracing is constant, which are suitable for commercial applications.

Cloud-assisted vehicular delay tolerant networks (DTNs) have been utilized in wide-ranging applications where a continuous end-to-end connection is unavailable, the message transmission is fulfilled by the cooperation among vehicular nodes and follows a store-carry-and-forward manner, and the complex computational work can be delegated to the disengaged vehicles in the parking lots which constitute the potential vehicular cloud. Nevertheless, the existing incentive schemes as well as the packet forwarding protocols cannot well model continuous vehicle collaboration, resist vehicle compromise attacks and collusion attacks, leaving the privacy preservation issues untouched. In this paper, a novel threshold credit-based incentive mechanism (TCBI) is proposed based on the modified model of population dynamics to efficiently resist the node compromise attacks, stimulate the cooperation among intermediate nodes, maximize vehicular nodes’ interest, and realize the fairness of possessing the same opportunity of transmitting packets for credits. Then, a TCBI-based privacy-preserving packet forwarding protocol is proposed to solve the open problem of resisting layer-adding attack by outsourcing the privacy-preserving aggregated transmission evidence generation for multiple resource-constrained vehicles to the cloud side from performing any one-way trapdoor function only once. The vehicle privacy is well protected from both the cloud and transportation manager. Finally, formal security proof and the extensive simulation show the effectiveness of our proposed TCBI in resisting the sophisticated attacks and the efficiency in terms of high reliability, high delivery ratio, and low average delay in cloud-assisted vehicular DTNs.

Distributed m-healthcare cloud computing system significantly facilitates efficient patient treatment for medical consultation by sharing personal health information among healthcare providers. However, it brings about the challenge of keeping both the data confidentiality and patients’ identity privacy simultaneously. Many existing access control and anonymous authentication schemes cannot be straightforwardly exploited. To solve the problem, in this paper, a novel authorized accessible privacy model (AAPM) is established. Patients can authorize physicians by setting an access tree supporting flexible threshold predicates. Then, based on it, by devising a new technique of attribute-based designated verifier signature, a patient self-controllable multi-level privacy-preserving cooperative authentication scheme (PSMPA) realizing three levels of security and privacy requirement in distributed m-healthcare cloud computing system is proposed. The directly authorized physicians, the indirectly authorized physicians and the unauthorized persons in medical consultation can respectively decipher the personal health information and/or verify patients’ identities by satisfying the access tree with their own attribute sets. Finally, the formal security proof and simulation results illustrate our scheme can resist various kinds of attacks and far outperforms the previous ones in terms of computational, communication and storage overhead.

Smart Grid technologies are ever-increasingly being adopted by nations and governments, primarily for a huge technological advancement in the areas of power management and information and communications. The smart meters deployed in a household can monitor electricity usage in real-time, and aggregate the data for further analysis and control; however, the existing solutions mainly depend on Paillier's additive homomorphic encryptions, which are of high computational complexity. This inefficiency makes them impracticable in smart grid, which embraces thousands of users and requires frequent data aggregation. In this paper, we propose a privacy-preserving aggregation framework, followed by a concrete construction using Elgamal encryption, which is secure under chosen plaintext attack (CPA) but not chosen ciphertext attack (CCA). Then, we further extend the concrete construction into a CCA-secure counterpart. In addition to efficient data aggregation, the proposed scheme can protect the user's meter data from sophisticated attacks, which are sponsored by the community gateway and the users. The formal security proof and performance evaluations illustrate the efficiency and the practicability of our scheme as well as its strong security.

ABSTRACT Digital signatures are one of the fundamental security primitives because they provide authenticity and nonrepudiation in the broadcast/multicast communication networks. However, the current broadcast/multicast authentication standards are vulnerable to signature flooding because excessive signature verification requests exhaust the computational resource of victims. The situation becomes worse in the case of the energy‐constrained networks such as wireless sensor networks and mobile ad hoc networks. As an essential variation of ordinary digital signature schemes, multisignature schemes enable a single compact signature to authenticate a message under a set of different signers. In this paper, we first propose an efficient identity‐based multisignature scheme with three interactive rounds under quadratic residue assumption, which equals to the large integer factoring assumption. By using the technique of quadratic residue‐based multiplicatively homomorphic equivocable commitment, an advanced identity‐based multisignature scheme is proposed to achieve to reduce the interactive round complexity to two rounds. We give the formal security proof that our schemes are existentially unforgeable under adaptively chosen message attacks and chosen identity attacks in the random oracle model. Compared with the previous work, our schemes are very efficient. In particular, our schemes are featured by the weak assumption and the efficient signing and verification procedures. Copyright © 2012 John Wiley & Sons, Ltd.

The focus of this paper is to design an efficient certificateless threshold signature scheme (CLTHS) for data report authentication in mobile ad-hoc network, by which relatively small subsets of very large groups can report data that is aggregated and ``certified" by means of a signature. Examples include vehicular networks where cars report traffic conditions, sensor networks that report aggregate data such as temperature or radiation levels, military devices transmitting information to be reported to various commands, and more. We first define the security notion for certificateless threshold signature. Different from the adversarial model defined by Wang et al., our definition considers Type I and Type II adversary separately. At the same time, we develop the simulatability and relationship between certificateless threshold signature and the underlying certificateless signature. We also propose the first CLTHS scheme which is proved existentially unforgeable against adaptively chosen message attack without random oracles. Compared with the previous scheme, our scheme is much more efficient.

Abstract In electronic communication and wireless communication, message authentication should be necessary. However, traditional method message authentication code (MAC) employs a symmetric cryptographical technique and it needs to keep a shared private key between two parties. For convenience, people now begins to use public key techniques to provide message authentication. In wireless communication, we shall save more space for message itself because of the limited resources. Therefore, we believe that our proposed digital signature scheme will be more fitful for this kind of communication due to the following merits: (1) in addition to inheriting the merits of RSA signature such as high verification efficiency, the proposed scheme also shows its advantage over RSA by resisting low public key exponent attack; (2) comparing with 1024 bits RSA, our digital signature scheme can sign 2048‐bit long message once, and generate a signature with 1025 bits length which doubles the capacity of the 1024‐bit RSA signature; (3) the scheme is provably secure and its security is tightly related to the hardness of conic‐based (CB)‐RSA assumption. Copyright © 2008 John Wiley & Sons, Ltd.

In this paper, we discuss identity-based encryption in multiple PKG environment. We formulate the security notions for multi-receiver multi-PKG identity-based encryption (IBE) and present an efficient multi-receiver IBE scheme which is proven secure against a new hard problem called N-xDBDH in random oracle model.

The focus of this paper is to design an efficient and secure solution addressing the key escrow problem in proxy re-signature schemes, i.e., the proxy knows the user's private key, which damages the essential requirement - "non-repudiation" property of proxy re-signature schemes. In this paper, we first define the security model for threshold proxy re-signature scheme, and then propose two threshold proxy re-signature schemes based on Ateniese's (see ACM CCS, p.310-319, 2005) and Shao Jun's (see Progress in Cryptology - INDOCRYPT 2007, vol.4859 of LNCS, p.197-209, 2007) approach. To our knowledge, there is no threshold proxy re-signature scheme that has been formally presented yet.

Abstract Secure and authenticated message transmission is one of the major aims of mobile communication security research. To achieve this goal, a typical methodology in mobile communication is a mobile device and its server first establish a session key, then use the session key to encrypt all subsequent messages. However, establish such an authenticated session key itself is a hard work, and only use the session key to encrypt subsequent messages also cannot provide nonrepudiation. Therefore, there is a desire to seek an alternative method. In this paper, we would like to propose a new authenticated encryption protocol for mobile communication. The proposed protocol, compared with other authenticated encryption protocols, will simultaneously achieve confidentiality, integrity, authentication, nonrepudiation, and perfect forward secrecy. Copyright © 2006 John Wiley & Sons, Ltd.