The rapid development of Internet of Things technology has promoted the popularization of Internet of Vehicles, and its safety and reliability have become the focus of intelligent transportation system research. Vehicle-road collaboration relies on the collaborative computing and storage resources of the vehicle on-board unit (OBU), which are usually limited. When the vehicle in the edge area needs to do computing tasks such as intelligent driving, but its own computing resources are insufficient. Therefore, it needs other computing resources from idle vehicles and road side unit (RSU). This resource sharing can get additional computing resources to complete the task, and can be more convenient to complete the computing task quickly. Most current studies consider this type of resource sharing as a vehicle-to-vehicle (V2V) network transaction, aiming to stimulate the enthusiasm of vehicle sharing and optimize the utilization of computing resources in edge areas. However, the traditional blockchain transaction mode exposes serious privacy disclosure risks in vehicle networking resource transactions, including the openness and transparency of user identity, transaction details, and transaction addresses, which poses great challenges to data security. Therefore, this study innovatively proposed a blockchain-based privacy protection scheme for vehicle networking resource transaction details. By introducing committed value protection, zero-knowledge proof technology and constructing temporary transaction addressed mechanism. The scheme realized the comprehensive privacy protection of transaction funds, transaction details and transaction addresses, which could effectively avoid the disclosure of users' sensitive information. Compared with the existing methods, the proposed scheme not only greatly enhanced the privacy protection capability, but also ensured the efficiency and security of transaction verification through zero-knowledge proof, avoiding the direct exposure of private keys. Meanwhile, the experimental verification demonstrates that the scheme not only enhances the level of privacy protection but also does not augment the supplementary processing burden. Furthermore, it is evident that the scheme meets the rigorous requirements for real-time resource transactions in the Internet of Vehicles.
Machine learning has achieved remarkable results in numerous fields, demonstrating strong momentum and promising prospects for future development. However, machine learning is facing issues related to data security. User data contains a vast amount of sensitive personal information, and once privacy is breached, users may not only suffer from harassment but also face threats to their lives and property security. As a result, users’ willingness and trust in sharing local raw data are gradually decreasing. In response to this situation, federated learning technology has emerged, which enables efficient training of decentralized data through distributed machine learning methods while protecting users’ data privacy. Traditional federated learning systems suffer from issues such as single points of failure and lack of trust. Blockchain, as a decentralized, traceable, and tamper-resistant distributed ledger technology, provides a new solution for federated learning. It records every update of the global model, verifies and tracks local updates, and is equipped with a fair incentive mechanism. Based on these ideas, this paper proposes a federated learning framework combined with blockchain, aiming to address data security issues in federated learning.
This study explores how blockchain technology can be strategically applied to improve data security and privacy across key sectors, including healthcare, finance, and supply chain management. Through a modular, Python-based implementation using permissioned blockchains, the research evaluates performance metrics such as transaction speed, data integrity, encryption, and anomaly detection. Results indicate that blockchain systems significantly enhance data protection by ensuring immutability, decentralization, and real-time validation. In healthcare, the system effectively flagged anomalous records; in finance, it supported secure, high-volume transactions; and in supply chains, it enabled transparent, tamper-proof tracking. Despite promising outcomes, challenges such as interoperability, scalability, and regulatory compliance remain. The study also highlights ethical considerations, particularly in balancing transparency with privacy. Overall, blockchain emerges as a robust and adaptable solution for modern data security needs. The findings provide a foundation for further research and practical guidance for organizations seeking to adopt blockchain in sensitive and high-risk data environments.
Abstract This paper evaluates Dynamic-IoTrust access control that integrated blockchain and trust value to meet the requirements of dynamic, secure, and distributed access control in the IoT environment. Dynamic-IoTrust intended to overcome the issues related to dynamic access control in IoT by limit authorized users’ access based on the trust value and user misbehavior. In particular, the system contains three kinds of smart contracts, multiple Main Smart Contract (MSC), one Register Contract (RC), and one Judging Contract (JC). Dynamic-IoTrust provides predefined static policy and dynamic trust value. The performance of Dynamic-IoTrust is analyzed by calculating the cost consumption rate of smart contracts and their function. A comparison is made between the existing systems and Dynamic-IoTrust. The results illustrate the transaction and execution costs of smart contracts.
This systematic literature review compares two leading selective-disclosure primitives for decentralised identity-BBS+ signatures and Selective-Disclosure JSON Web Tokens (SD-JWT)-to clarify their suitability for privacypreserving credentials. Following Kitchenham's protocol, 226 records from 2017-2025 were screened across IEEE, ACM, SpringerLink, ScienceDirect, IETF and W3C repositories, yielding 31 primary studies with empirical data. Quantitative synthesis shows that BBS+ derived proofs remain constant-size at roughly 140 bytes and verify in about 12 ms on consumer hardware, whereas SD-JWT presentations grow with the number of revealed claims but still verify in under 10 ms for typical twoclaim use cases. Qualitative analysis confirms BBS+ provides strong unlinkability, predicate proofs and zero-knowledge disclosure, while SD-JWT offers seamless integration with existing JOSE/OAuth infrastructures yet carries correlation risk due to stable salted digests. Standardisation progress is comparable: the BBS+ cryptosuite reached W3C Candidate Recommendation in April 2025, and SD-JWT is in late-stage IETF review. The review concludes that privacy-critical scenarios such as age-gated services favour BBS+, whereas high-throughput web applications benefit from SD-JWT; consequently, hybrid wallet support for both formats is recommended. Future research should tackle scalable revocation, post-quantum migration and multi-credential aggregation to sustain long-term trust and interoperability.
Secure machine learning paradigms have emerged as compelling solutions to address growing concerns of large-scale data collection in modern Machine Learning (ML) systems. These paradigms leverage secure computation techniques to enable the execution of ML applications without the necessity to share raw data, models or predictions to be shared between parties, offering strong, formal privacy guarantees. Recent advances have significantly enhanced both the scalability and expressiveness of these secure paradigms, facilitating their deployment in real-world scenarios across a variety of privacy-sensitive domains. However, the very mechanisms that provide these privacy guarantees also introduce new challenges to robustness, trust, and accountability. To ensure secrecy, secure ML systems conceal the processes of training and inference, making them difficult to inspect, validate, or audit. This intrinsic opacity creates a fundamental tension between privacy and accountability: hiding data and models to protect users’ privacy can also obscure failures and enable undetectable manipulation. Furthermore, in many secure ML frameworks, multiple, potentially untrusted parties collaboratively contribute to computations, thereby amplifying risks. Traditional threat models in adversarial ML often depend on transparent access to data, models, or outputs—assumptions that do not hold in secure settings. As a result, these systems become vulnerable to new and sometimes more potent attack vectors. Without dedicated integrity mechanisms, these privacy-preserving systems cannot be safely deployed in high-stakes domains such as healthcare, finance, or critical infrastructure. Realizing the full potential of secure ML requires a comprehensive understanding of the unique threats these systems face, the development of new integrity mechanisms, and their integration into these systems in a way that is efficient and preserves the privacy guarantees they provide. This dissertation advances accountability in secure ML through two complementary directions. First, it develops an understanding of the robustness challenges that arise in secure settings. We investigate the role of memorization and system-level dynamics in exposing secure systems to targeted manipulation. Based on these insights, we then introduce new cryptographic building blocks to strengthen the robustness and transparency of secure ML. We present RoFL, a system for privacy-preserving input validation in secure Federated Learning; Arc, the first framework for end-to-end auditing of secure ML pipelines; and Artemis, a new construction for generating efficient zero-knowledge proofs for real-world ML models. Together, these contributions lay the foundation for secure ML systems that are not only private, but also accountable and trustworthy in practice.
The rapid evolution of digital identity verification demands solutions that balance security, privacy, and efficiency. The electronic know your customer (eKYC) is a technological integration for client identification. It automates the process, reducing costs related to traditional know your customer (KYC). This includes eliminating paper-based document management, reducing manpower needs, and minimizing human errors. This systematic literature review (SLR) uses the preferred reporting items for systematic reviews and meta-analyses (PRISMA) model to investigate the revolutionary potential of blockchain-based electronic KYC (eKYC), focusing on self-sovereign identity (SSI) and Decentralized Identifiers (DID). The evaluation summarizes the current state by critically assessing 44 selected research works from an initial pool of 367. Our findings show that decentralized eKYC improves security with tamper-proof credentials and cryptographic verification. SSI and DID give users control over their data and selective disclosure. However, there are key limitations: 1) a focus on financial applications, ignoring Internet of Things (IoT) integration; 2) a lack of comprehensive technical analysis on scalability and interoperability; and 3) limited real-world case studies on regulatory compliance and challenges. This work combines insights from research and industry, highlighting the need for regulatory collaboration, hybrid architectures for scalability, and user-centric design. In addition, most identity management solutions are based on Ethereum (33%), followed by Hyperledger (18%). Around 51% of solutions use smart contracts, with banking (23%) and the financial industries (19%) being the primary adopters. It emphasizes the importance of standardized eKYC protocols, technical evaluations, and interdisciplinary collaboration for practical adoption across sectors.
In the secure two-party computation (2PC), an adversary is often categorized as semi-honest or malicious, depending on whether it follows the protocol specifications. Covert security (Aumann and Lindell, 2010) first looks into the “middle ground”, such that an active adversary who cheats will be caught with a predefined probability. Other security notions, such as publicly auditable security (Baum et al., 2014) and (robust) accountability family (Küsters et al., 2010; Graf et al., 2023; Rivinius et al., 2022), achieve public verifiability as a stronger security guarantee by relying on heavy offline and online constructions with zero knowledge proofs and (or) a bulletin board functionality. In this work, we propose a new security notion called honorific security, where an external arbiter can identify the cheater without a bulletin board. Specifically, we delay and outsource the verification steps to the arbiter, so that the original online computation is thus accelerated. We show that a maliciously secure garbled circuit (GC) (Yao, 1986) protocol can be constructed with only slightly more overhead than a passively secure protocol. Our construction performs up to 2.37 times and 13.30 times as fast as the state-of-the-art protocols with covert and malicious security, respectively.
The group signature with designated traceability (GSdT) is a kind of group signatures (GS) which aim to restrict the opening authority of the group manager; by setting an access structure over openers' attributes at the signing, a signer is able to control openers who can open the signature.A generic construction of GSdT was given when the notion was introduced, then a pairing-based construction and a symmetric-key-based one were presented.Nonetheless, it remains open whether a post-quantum GSdT with full anonymity can be truly constructed.In this paper, we give a lattice-based GSdT scheme that has full anonymity for the first time.In our construction, the lattice-based ciphertext-policy attribute-based encryption (CP-ABE) by Tsabary and the lattice-based group signatures (GS) by Libert et al. are employed.The CP-ABE is based on the Regev public-key encryption, while the GS uses a non-interactive zero-knowledge proof to prove the correctness of the encryption in the signing process.Based on the compatibility, we combine and modify them to build up a GSdT scheme.
Blockchain technology has been envisioned as an emerging facilitator of auditable, transparent, and secure electronic voting (e-voting) systems to overcome issues with traditional and electronic voting systems. However, preserving data integrity, offering voter privacy, and scalability in blockchainbased e-voting systems are persistent issues. In this systematic literature review of peer-reviewed research articles from 2018 to 2025, this paper explores cryptographic schemes, architecture designs for blockchain-based e-voting systems, and solutions for scalability. By taking an PRISMA-congruent structured research methodology approach, nine core studies are reviewed to discuss Zero Knowledge Proofs and blind signature schemes for maintaining privacy conservation, blockchain immutability to maintain integrity, and layer-2 scaling solutions to bypass throughput bottlenecks. Conclusions suggest that although transparency and audita-bility are elevated with applications of blockchain technology, implementation for massive-scale elections remains in its nascent stage and requires development in privacypreservation cryptographic schemes and scalable architecture designs. As a review paper, it compiles an updated summary of the status of the landscape of blockchain-based e-voting systems and highlights existing knowledge gaps and proposes research directions for developing secure, scalable, and privacy-respecting digital elections.
Open access
3 source records
Blockchain Technology Applications and Security
Internet Traffic Analysis and Secure E-voting
Advanced Steganography and Watermarking Techniques
Background: Large Language Models (LLMs) like ChatGPT-4 Turbo, Claude 4 Sonnet, and DeepSeek-V3 are foundational to modern AI applications. However, a significant gap exists in understanding the direct link between their technical performance and user engagement, their scalability under concurrent load, and the practical performance cost of emerging privacy-preserving technologies. Objectives: This thesis conducts a holistic evaluation of these three leading LLMs to: (1) Compare their performance across latency, accuracy, and client-side resource utilization, and establish the relationship between these metrics and qualitative user engagement scores in various conversational contexts (RQ1). (2) Determine their scalability limits under concurrent user loads and quantify the performance overhead of integrating a zero-knowledge proof privacy protocol (EZKL) (RQ2). Methods: A custom, containerized Python framework was used to systematically test the models. For RQ1, performance and engagement were evaluated in three structured contexts: multi-turn (testing memory), cohesive (testing consistency), and ethical (testing safety) sessions. For RQ2, scalability was measured using Locust to simulate 25 to 200 concurrent users in both a standard centralized setup and a privacy-enhanced EZKL configuration. Key metrics included throughput (RPS), error rates, latency (median and P99), client-side resource consumption, and ZKP generation/verification times. Results: For RQ1, ChatGPT-4 Turbo emerged as the top generalist, showing the best balance of low latency, high accuracy, and strong engagement scores in dynamic multi-turn sessions (e.g., 7.9 personalization score). Claude 4 Sonnet excelled in specialized tasks, achieving a perfect context-switching score (0.0) in cohesive sessions and the highest Harm Avoidance Score (8.0) in ethical sessions, albeit with higher resource usage. DeepSeek-V3 consistently showed the highest latency and resource consumption, negatively impacting its engagement scores. For RQ2, ChatGPT-4 Turbo was the most scalable, peaking at 210 RPS with the lowest error rate. The integration of the EZKL protocol resulted in a catastrophic performance collapse for all models, with throughput dropping to near-zero and latency increasing to hundreds of thousands of milliseconds, rendering it unviable for real-time applications. Conclusions: The study concludes that model selection is highly use-case dependent: ChatGPT-4 Turbo is optimal for scalable, general-purpose applications; Claude 4 Sonnet is superior for high-stakes tasks requiring safety and precision. The findings empirically demonstrate that superior technical performance is a direct enabler of higher user engagement. Finally, current zero-knowledge proof implementations impose a prohibitive performance cost for interactive, scalable AI systems.
Open access
Artificial Intelligence in Healthcare and Education
Muhammad Shoaib Farooq, Shahzada Fahad Munir, Muhammad Faraz Manzoor, Momina Shaheen
The increasing complexity of fraudulent activities requires advanced fraud detection systems, as existing solutions lack effectiveness due to two challenges. First, privacy concerns prevent financial institutions from sharing sensitive transaction data. Second, data imbalance causes biased models, as fraudulent transactions represent a small fraction of total transactions, leading to poor fraud detection performance. To address these challenges, we propose an AI‐driven adaptive federated learning (AFL) framework for credit card fraud detection (CCFD). AFL enables decentralized learning, allowing financial institutions to train a global fraud detection model collaboratively without sharing raw transaction data. The model aggregation is performance‐adaptive, weighting client contributions based on detection accuracy to ensure a robust global model. To overcome data imbalance, we introduce a multistep data balancing framework integrating Tomek links for undersampling, borderline‐SMOTE for oversampling, and cognitive sample pruning to remove misleading samples. To evaluate the robustness and generalizability of the proposed framework, we conducted experiments on both the widely used 2013 Kaggle dataset and the Sparkov simulated dataset (2019‐2020). The Sparkov dataset, which contains interpretable demographic and merchant‐level features, allowed us to test the model’s adaptability to diverse data sources. The results demonstrate that the proposed AFL framework, combined with advanced data balancing, significantly outperforms traditional models, achieving 99% accuracy, 99.5% precision, 99.4% recall, and 99% F1‐score on the Kaggle dataset, and 97.4% accuracy, 99.5% precision, 97.5% recall, and 98.4% F1‐score on the Sparkov dataset. This research highlights AI’s transformative role in finance, particularly in enhancing fraud detection systems with improved accuracy, robustness, security, and scalability.
Federated learning (FL) has emerged as an efficient machine learning (ML) method with crucial privacy protection features. It is adapted for training models in Internet of Things (IoT)-related domains, including smart healthcare systems (SHSs), where the introduction of IoT devices and technologies can arise various security and privacy concerns. However, as FL cannot solely address all privacy challenges, privacy-enhancing technologies (PETs) and blockchain are often integrated to enhance privacy protection in FL frameworks within SHSs. The critical questions remain regarding how these technologies are integrated with FL and how they contribute to enhancing privacy protection in SHSs. This survey addresses these questions by investigating the recent advancements on the combination of FL with PETs and blockchain for privacy protection in smart healthcare. First, this survey emphasizes the critical integration of PETs into the FL context. Second, to address the challenge of integrating blockchain into FL, it examines three main technical dimensions such as blockchain-enabled model storage, blockchain-enabled aggregation, and blockchain-enabled gradient upload within FL frameworks. This survey further explores how these technologies collectively ensure the integrity and confidentiality of healthcare data, highlighting their significance in building a trustworthy SHS that safeguards sensitive patient information.
Federated learning represents a transformative approach in the realm of machine learning by enabling the training of models across decentralized devices while maintaining data privacy. Traditional centralized learning methods often compromise user privacy and data security by requiring the aggregation of data on a central server. In contrast, federated learning decentralizes the training process, allowing devices to collaboratively learn a shared model without exposing their private data. This paper explores the intricacies of federated learning, emphasizing its potential to enhance privacy and efficiency in AI systems. We delve into the technical architecture of federated learning, discussing key components such as data partitioning, model aggregation, and communication protocols. Furthermore, we address the challenges associated with federated learning, including data heterogeneity, communication overhead, and model convergence. Through comprehensive analysis and case studies, we demonstrate the efficacy of federated learning in various applications, from healthcare to finance. Our findings underscore the critical role of federated learning in safeguarding data privacy while optimizing the performance of machine learning models. As the demand for privacy preserving technologies continues to grow, federated learning emerges as a pivotal solution, paving the way for more secure and efficient AI systems.