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.

In privacy-preserving federated learning (PPFL), the local gradient updates are encrypted to protect the privacy of the clients. However, this opacity hinders the effective detection of the poisoning attacks. Although the existing defense methods can offer robust privacy guarantees, they often require additional rounds of interaction, which significantly increase the computational and the communication overhead, thereby limiting the practicality and the scalability of federated learning deployments. To address the trade-off between privacy protection and defense efficiency, we propose SurroFL, a robust and efficient privacy-preserving federated learning framework designed to defend against the poisoning attacks. SurroFL uses the locality sensitive hashing (LSH) to generate the sketches of the encrypted gradients, enabling the detection of malicious behaviors in the plaintext domain without compromising privacy. In addition, SurroFL adopts the sparse batch encryption strategy to reduce the encryption overhead and incorporates a dynamic reputation-based scoring mechanism to adaptively suppress the malicious clients over time. Our extensive experiments, conducted under the IID data partition setting on the multiple benchmark datasets and the various poisoning attack scenarios, demonstrate that SurroFL consistently outperforms the existing approaches in both robustness and efficiency. Notably, SurroFL achieves an improvement of 20%-70% in the model accuracy while substantially reducing both the computational and the communication overhead, making it highly suitable for deployment in the resource-constrained federated learning environments.

HotStuff is a pipelined Byzantine Fault Tolerance (BFT) algorithm that has the good properties of both linear communication complexity and optimistic responsiveness. However, its three-chain model has poor robustness against the performance attacks, particularly the forking attack. Some recent works have employed the two-chain model to enhance the robustness, but they either lose the optimistic responsiveness or introduce huge latency. In our work, we present Short-HotStuff, a two-chain BFT algorithm with linear communication complexity and optimistic responsiveness. In the two-chain model without waiting for a maximum network delay, the leader has to convince the other nodes that its proposed block is on a safe path when the previous leader fails, otherwise, the hidden lock will cause the liveness issue. Contrary to some existing BFT algorithms that rely on broadcasting multiple Quorum Certificates (QCs) or introduce extra phases, our Short-HotStuff requires the leader to broadcast the highest QC, as well as the evidence performed by a threshold signature with only a constant size, and such evidence can prove that its proposed block is indeed extended from a safe path. We have conducted the experiments to compare our Short-HotStuff with HotStuff, Marlin and Fast-HotStuff. The results indicate that compared with HotStuff, Short-HotStuff achieves a 35% reduction in average latency without the leader failing, and doubles the average throughput in the face of forking attacks. In the event of the leader failing, the average latency of Short-HotStuff is reduced by 43% and 82% respectively compared with Marlin and Fast-HotStuff.

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.

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.

Blockchain technology has always been plagued by performance problems. Given this problem, many scaling schemes have been put forward. A layer 2 network is a technology that solves the performance problem of blockchain. Connected parties in this network can set up channels to send digital currency to each other. Since the interaction with the blockchain is only required when the channel is established and closed, a large number of transactions do not need to be recorded on the blockchain, thus reducing the blockchain capacity. Due to the special structure of the payment channel, the distribution of funds in the channel is often unbalanced, which limits the route payment to a certain extent. This paper improves the original payment method in the second layer network by introducing new scripts. The new payment scheme supports proof of payment which is integral to the nature of the lightning network and divides the payment into several subpayments, so the large payment can be divided into relatively small payments. Due to the capacity limitation of the payment channel, theoretically, the success rate of the micropayment route is higher. This paper tests the new payment scheme on the simulated network and validates the nature of this solution to have a high routing success rate while supporting proof of payment.

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 .

Complexity of sequences plays an important role in pseudorandom sequences and cryptography. In this paper, we present a construction of sequences with high nonlinear complexity from function fields. The main idea is to make use of function fields with many rational places as well as an automorphism of large order. We illustrate our construction through rational function fields and cyclotomic function fields in which there exist some automorphisms of large order. It turns out that we are able to: 1) slightly increase the length of the inversive sequence without losing nonlinear complexity and 2) obtain sequences with much larger nonlinear complexity than random sequences.

Abstract Generally, the information theoretically secret key agreement by public discussion consists of three phases, namely, advantage distillation, information reconciliation, and privacy amplification. In the existing literatures, these three phases are studied separately, and no comprehensive consideration has been given. In this paper, we analyze the restrictive relationship among these three phases from an overall point of view. For the satellite scenario, we analyze in detail the Winnow protocol for the first time. We also present the mutual restrictive relationship between the parameters of the advantage distillation phase and the information reconciliation phase. We further address how to set the parameters of the advantage distillation and the information reconciliation to maximize the total secret key agreement efficiency. Moreover, we determine the required length range of the initial random key string to guarantee the validation of the secret key agreement. Copyright © 2015 John Wiley & Sons, Ltd.

Hyperelliptic curves have been widely researched for cryptographic applications, and some special hyperelliptic curves are often considered for practical applications. For efficient implementation of hyperelliptic curve cryptosystems, it is crucial to have efficient scalar multiplication in the Jacobian groups. For the hyperelliptic curve C q : v 2 = u p − au − b over the field $\Fopf_{q}$ with q a power of an odd prime p, Duursma and Sakurai (2000) presented a scalar multiplication algorithm for q = p, a = 1 and b ∈ $\Fopf_{p}$. In this paper, by introducing the concept of simple divisors, we prove that a general divisor can be decomposed into the sum of some simple divisors. Based on this fact, we present a formula for p-scalar multiplications for any reduced divisor, then we give two efficient algorithms to speed up scalar multiplications for any parameters a and b over any extension of $\Fopf_{p}$. Compared with the signed binary method, the computations of our algorithms cost 55% to 76% less.

Hyperelliptic curves of genus ≤ 3 over finite fields have been researched and recommended for cryptography for about twenty years. Though the hyperelliptic curves over finite fields of genus four may been not secure for general cryptographic applications, such as digital signature systems, but some special hyperelliptic curves of genus four may have some privileges when they are applied in pairing-based cryptosystems. Hyperelliptic curve classification based on isomorphism is helpful for the selection of secure hyperelliptic curves over finite fields for practical cryptosystems. The isomorphism classes of hypereilliptic curves of genus 2 or 3 over finite fields have been studied in previous works. Here, the number of isomorphism classes of hyperelliptic curves of genus 4 over finite fields with the characteristics larger than three is given.

Leaf Area Index (LAI) is a very important parameter in the area of vegetation quantitative remote sensing. Large range of LAI can reflect the change of eco-system. This article has discussed whether the crop type is a factor to impact the leaf area index retrieval. We choose four types of crops in our research and Hyperspectral Data and leaf area index of these crops were measured. Then the LAI retrieval models were established, which demonstrate the relationships between SVI and LAI. Finally the conclusion can be made that the type of crop is a factor impacting the LAI retrieval. For different crops, the best models are not the same. But the little difference of R2can be omitted. The SR is the best spectral vegetation index for LAI retrieval.

In the field of drought monitoring using remote sensing, the software has been lagging behind the applications. For the operational needs of monitoring and evaluating drought, a professional remote sensing application system (named RSDMS) is designed and developed with the concept of component-based technology. Starting from the architecture design, this article details the component-based design patterns of this system, and then discusses some key techniques during the development process. After achieving the general-purpose functionalities, this system also integrates a variety of drought monitoring models in the form of component, showing great flexibility. Meanwhile, the results of preliminary application in Ningxia region of northwestern China are presented to indicate that the system has a good practice effect.

A comparative research of snow cover is conducted, which is aimed to investigate the effect of spatial scale variation and the differences of different spatial resolution remote sensing images, by using moderate-resolution imaging spectroradiometer (MODIS) data and the satellite-B (HJ-1B) data of the small satellite constellation for environment and disaster monitoring and forecasting of China. Results show that: (1) the scaling-change within certain limits has little impact on snow cover mapping; (2) high spatial resolution image gives a better description of the detailed information of snow cover area; (3) the differences of snow cover between low spatial resolution and high resolution images are due to the different snow cover type.

Drought is a harmful and headachy natural disaster in the world, which has caused considerable loss to agricultural production and economy per year with high frequency. It is therefore very important and necessary for drought monitoring over large scales by remotely sensed techniques. In order to establish physically meaningful water stress index, the SWIR bands with strong water absorption features are used in this study, then the shortwave infrared water stress indices (SIWSI) are constructed using NIR and SWIR bands. A representative arid and semi-arid region over northwestern China, Ningxia region, where droughts occur frequently, is selected to assess the drought status using two indices of SIWSI6, 2and SIWSI7, 2derived from NIR channel 2 and SWIR channel 6 or 7 of Moderateresolution Imaging Spectroradiometer (MODIS) sensor, combining with ground truth data of soil moisture observed by the meteorological stations across the whole study area. The fitted regressions of negative exponential curves indicate that both indices have significantly correlated with the in-situ measurements (P6, 2performs slightly better than the SIWSI7, 2(r2=3D0.56 and 0.48).