Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7×–7.0× speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.

Addressing the challenge of the security recognition through electroencephalogram (EEG) biometrics, we propose an EEG recognition system named EEG-FE rrRS. Leveraging a fuzzy extractor, this system aims to facilitate personalized recognition in the scenarios such as the unmanned aerial vehicle (UAV) and metaverse that require human-computer interaction. This robust and reusable fuzzy extractor framework capitalizes on EEG characteristics for biometric identification and it can be divided into two parts: an EEG signal processing module and a proprietary fuzzy extractor scheme containing the secure sketch and the strong extractor. By employing EEG-FE rrRS, a unique digital identity can be established for each user. The security of the proposed fuzzy extractor is proved from the perspective of entropy loss. Furthermore, the simulations have been conducted to evaluate the performance of EEGFE rrRS, showcasing its highly promising recognition accuracy. Specifically, the recognition rate of the system on the motor imagery database has reached 0.92% FRR and 0.08% FAR, respectively. While on the SEED database, the recognition rate has achieved 0% FRR and 0% FAR, respectively.

Biometric authentication is a very convenient and user-friendly method. The popularity of this method requires strong privacy-preserving technology to prevent the disclosure of template information. Most of the existing privacy protection technologies rely on classic encryption techniques, such as homomorphic encryption, which incur huge system overhead and cannot be popularized. To address these issues, we propose a novel biometric authentication scheme with privacy protection based on support vector machine and zero knowledge proof (BioAu–SVM+ZKP). BioAu–SVM+ZKP allows users to authenticate themselves to different service providers without disclosing any biometric template information. The evidence is generated through the zero-knowledge proof utilizing polynomial commitments. Our approach for generating a unique and repeatable biometric identifier from the user’s fingerprint image leverages the multi-classification property of SVM. Notably, our scheme not only reduces the communication overhead but also provides the privacy protection features. Besides, the communication overhead of BioAu–SVM+ZKP is constant. We have simulated the authentication scheme on the common dataset NIST, analyzed the performance and proved the security.

Currently, biometric-based authentication schemes have been widely deployed in the mobile edge computing environment to ensure the authenticity of the edge nodes’ identities. While some solutions for mobile edge computing adopting a tripartite architecture employ anonymous authentication to protect the edge nodes’ sensitive biometric data and transaction information, these solutions come with their own set of challenges. Specifically, the service provider is unable to hold the edge nodes accountable for any violation committed during the transactions. Moreover, the edge nodes are unable to revoke their identity information stored in the registration center. To address these issues, in this work, we introduce a secure and efficient biometric-based anonymous authentication scheme for mobile edge computing. Our approach utilizes non-interactive zero-knowledge (NIZK) arguments and linear encryption to achieve anonymity and traceability of the edge nodes’ identities, while also using accumulators to implement the revocability of the edge nodes’ identities, ensuring that the edge nodes with revoked identities cannot pass the verification of the service provider. Additionally, we design an efficient batch verification protocol to handle anonymous authentication messages for large-scale edge nodes. The feasibility of the proposed scheme could be proven positively by both of the experimental results and the security analysis. The experimental results show that our approach requires up to 418 ms to create an anonymous identity and 16.2 ms to verify it. The time consumption for batch verification of the anonymous identities of the 1,200 edge nodes is only 54.4 ms.

The volume of the data generated by the Internet of Things (IoT) has been expanding rapidly, primarily driven by the personal devices. However, with the severely limited memory resources and difficult security authentication processes, large amounts of the data must be abandoned. Blockchain of Things (BCoT) provides a scalable solution to these challenges by integrating blockchain and the IoT. In this paper, we propose an efficient BCoT-based data trading system using physical unclonable functions (PUFs) for authentication. PUFs are utilized for iterative keys and pseudo-identity generation to ensure the privacy and security of the IoT devices. To protect the copyright of the sellers’ data, the system sets up a data arbitration center to address the issues related to the unauthorized resale of the datasets. In addition, a fuzzy comprehensive evaluation model is proposed to regulate the behavior of the data traders. Our security analysis demonstrates that the proposed system is not only secure under the ROR model but is also resistant to the internal attacks. The experimental results show the reliability and the effectiveness of the proposed system.

It is crucial to ensure the privacy and authenticity of patients’ medical data in the medical insurance claim process, but in the current medical insurance claim process, there are some problems such as low efficiency, complex service, unreliable data and data leakage. Therefore, considering the privacy and sensitivity of patients’ medical data, we can improve the current issues by employing blockchain, smart contracts and zero-knowledge proof technology. In this paper, we propose a novel medical insurance claim scheme based on smart contracts, blockchain and zero-knowledge proof. Our scheme mainly involves two scenarios: medical insurance purchasing and medical insurance claiming. In the privacy-preserving transaction phases of the two scenarios, we can ensure the legitimacy and privacy of the transactions between the patients and the insurance companies by using a non-interactive zero-knowledge proof and the homomorphic encryption algorithm under the Decisional Bilinear Diffie–Hellman (DBDH) assumption. In the identity privacy-preserving phases of the two scenarios, we can ensure the legitimacy and the privacy of patients’ identities by integrating Schnorr protocol and Fiat–Shamir heuristic method. The security analysis, the computation cost and the communication cost of our scheme are given. Compared with our referred schemes, the performance evaluation shows that our scheme not only meets the requirements of the legality of the medical insurance claim, but also ensures the authenticity and privacy of the patients’ medical data. Moreover, the experimental results demonstrate that our scheme is feasible and has an acceptable time overhead.

To address the camera origin identification problem of inquiry images, many forensic methods have been proposed. However, the heavy computational overhead and the potential threat of privacy leakage for inquiry images make many existing forensic methods less applicable. Only a few research works have proposed secure forensic methods to address the aforementioned issues; however, they did not give a detailed analysis for the statistical performance. In this paper, we propose an efficient privacy-preserving forensic method with analytical statistical performance to solve the camera model identification problem efficiently and securely. To preserve the privacy of inquiry images, we propose a hybrid privacy-preserving scheme consisting of two operations:Position Scrambling Encryptionto preserve the privacy of image content andNoise Linear-Mapping Processingto preserve the privacy of camera model identity for inquiry images. In the encrypted domain where the proposed privacy-preserving scheme is employed, we first propose a novel statistical noise model, which can accurately characterize an encrypted JPEG inquiry image. Then, a noise model-based detector is designed to identify different camera models. Experimental results verify the feasibility of our proposed method from both privacy-preserving and forensic effectiveness and report that our method outperforms the state-of-the-art secure forensic methods, especially when sample images used to estimate camera fingerprints are insufficient, such as only 2 available images.

In order to analyze real-time power data without revealing users’ privacy, privacy-preserving data aggregation schemes have been extensively researched in smart grid. However, most of the existing schemes either can only allow stationary users, or require a trusted center. In this paper, we propose an efficient and robust multidimensional data aggregation scheme based on blockchain. In our scheme, a leader election algorithm in Raft protocol is used to select a mining node from all smart meters to aggregate data. A dynamically verifiable secret sharing homomorphism scheme is adopted to realize flexible dynamic user management. In addition, our scheme can not only resist internal and external attacks but also support multidimensional data aggregation and fault tolerance. The security analysis shows that our proposed scheme is IND-CPA secure and can meet stronger security features. The experimental results show that compared with other schemes, our scheme can be implemented with lower computation and communication overhead.

In this paper, a construction of a fuzzy identity-based ring signature scheme (LFIBRS) is proposed. Our LFIBRS combines the characteristics of both the fuzzy identity-based signature (FIBS) and the ring signature. On the one hand, a signature issued under an identity ID can be verified by any identity ID ′ that is “close enough” to the identity ID . Since biometric identification is the well-known most popular and reliable identification method, our LFIBRS can be applied in such a situation whenever it is required for official audit or supervision that the signer’s real identity is needed to be authenticated. On the other hand, LFIBRS provides anonymity under the random oracle model. In addition, LFIBRS provides unforgeability under the small integer solution (SIS) lattice hardness assumption which can resist large-scale quantum computer attacks in the future.

In a multiserver architecture, authentication schemes play an important role in the secure communication of the system. In many multiserver authentication schemes, the security of the mutual authentications among the participants is based on the security of the registration center’s private key. This centralized architecture can create security risks due to the leakage of the registration center’s private key. Blockchain technology, with its decentralized, tamper-proof, and distributed features, can provide a new solution for multiserver authentication schemes. In a lot of multiserver authentication schemes, users’ permission is generally controlled by the registration center (RC), but these permission control methods cannot be applied in the decentralized blockchain system. In this paper, a blockchain-based authentication scheme for multiserver architecture is proposed. Our scheme provides a hierarchical authentication method to solve the problems of user permission control and user revocation caused by no registration center. The security of our scheme is formally proved under the random oracle model. According to our analysis, our scheme is resistant to attacks such as impersonation attacks and man-in-the-middle attacks. In addition, our performance analysis shows that the proposed scheme has less computation overhead.

In recent years, a lot of vulnerabilities of smart contracts have been found. Hackers used these vulnerabilities to attack the corresponding contracts developed in the blockchain system such as Ethereum, and it has caused lots of economic losses. Therefore, it is very important to find out the potential problems of the smart contracts and develop more secure smart contracts. As blockchain security events have raised more important issues, more and more smart contract security analysis methods have been developed. Most of these methods are based on traditional static analysis or dynamic analysis methods. There are only a few methods that use emerging technologies, such as machine learning. Some models that use machine learning to detect smart contract vulnerabilities cost much time in extracting features manually. In this paper, we introduce a novel machine learning-based analysis model by introducing the shared child nodes for smart contract vulnerabilities. We build the Abstract-Syntax-Tree (AST) for smart contracts with some vulnerabilities from two data sets including SmartBugs and SolidiFI-benchmark. Then, we build the Abstract-Syntax-Tree (AST) of the labeled smart contract for data sets named Smartbugs-wilds. Next, we get the shared child nodes from both of the ASTs to obtain the structural similarity, and then, we construct a feature vector composed of the values that measure structural similarity automatically to build our machine learning model. Finally, we get a KNN model that can predict eight types of vulnerabilities including Re-entrancy, Arithmetic, Access Control, Denial of Service, Unchecked Low Level Calls, Bad Randomness, Front Running, and Denial of Service. The accuracy, recall, and precision of our KNN model are all higher than 90%. In addition, compared with some other analysis tools including Oyente and SmartCheck, our model has higher accuracy. In addition, we spent less time for training .