Blockchain Papers

Mohan Raparthi

No abstract is available for this record.

Yue Wu, Jiahao Tian

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.

Amit Kumar, Neha Sharma, Korhan Cengiz, Simar Preet Singh

No abstract is available for this record.

Lycklama à Nijeholt, Hidde

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.

K. Deepa Thilak, K. Lalitha Devi, D. Poornima, K. Kalai Selvi · 6 authors

No abstract is available for this record.

Suhang Wei, Jinfang Jia, Xiang Feng, Huiqun Yu

No abstract is available for this record.

Edona Fasllija, Jakob Heher, Stefan More

No abstract is available for this record.

Santhosh Chitraju

No abstract is available for this record.

Xinzhong Liu, Jie Cui, Jing Zhang, Rongwang Yin · 8 authors

In vehicular networks, caching service content on edge servers (ESs) is a widely accepted strategy for promptly responding to vehicle requests, reducing communication overhead, and improving service experience. However, implementing such an architecture requires addressing the challenges associated with ES response data reliability and communication security. In this study, to tackle the ES response data reliability issue, a blockchain-assisted threshold signature scheme for cache-based vehicular networks is proposed. The scheme utilizes a threshold mechanism to sign the data broadcast by the ES, incorporates blockchain to trace malicious signers, and avoids the shortcomings and limitations associated with idealized assumptions for the ES in existing data-sharing schemes. Moreover, considering the communication security and high-speed mobility of vehicles, using the non-interactive signatures of knowledge based on the Σ-protocol, a secure and efficient message authentication scheme for vehicles and ESs is provided. Through rigorous security proofs and comprehensive analyses, our scheme satisfies the communication security requirements of vehicular networks. By leveraging the JPBC library for performance analysis, the proposed scheme demonstrates advantages as concerns both computation and communication overheads compared to related schemes. Moreover, we implemented the proposed scheme on an Ethereum test network (i.e., Goerli) to validate its feasibility.

Istiaque Ahmed, Kentaroh Toyoda, Tadashi Nakano, Shoji Kasahara · 6 authors

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.

Talha Abdullah Punjabi, Ahmad Qadeib Alban, Mahmoud Barhamgi

No abstract is available for this record.

F Richard, George K. Agordzo

No abstract is available for this record.

Souheib Yousfi, Marwa Chaieb

No abstract is available for this record.

Tianxiang Dai, Yufan Jiang, Yong Li, Jörn Müller‐Quade · 5 authors

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.

Zeyu Yu, Liming Huang, Hua Tang, Chuming Guan · 7 authors

No abstract is available for this record.

Yingfei Yan, Sherman S. M. Chow, Lucien K. L. Ng, Harry W. H. Wong · 6 authors

No abstract is available for this record.

Hiroaki Anada, Masayuki Fukumitsu, Shingo Hasegawa

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.

Hu Xiong, Yaxin Zhao, Hourui Deng, Erqiang Zhou · 6 authors

Existing secure aggregation schemes in federated learning (FL) face challenges related to detecting poisoning attacks and managing dynamic membership updates. To address these limitations, this article proposes a robust and dynamic aggregation framework for FL (RDFL), a robust and dynamic aggregation framework for FL. RDFL integrates a trimmed median algorithm with noninteractive range zero-knowledge proofs, providing a tunable mechanism for detecting abnormal updates. Client behavior is evaluated through a dynamic reputation scoring module, with malicious clients being added to a revocation list. By incorporating revocable attribute-based encryption, RDFL supports dynamic user management, ensuring that only authorized participants can access or update the global model. In addition, RDFL employs Shamir’s secret sharing and a pseudorandom double-masking scheme to maintain aggregation accuracy and protect communication privacy despite client dropouts. Experimental evaluations on the Extended MNIST (EMNIST) and CIFAR-100 datasets demonstrate that RDFL achieves strong security, communication efficiency, and model accuracy, making it suitable for FL involving a large number of clients with dynamic participation.

Akwesi Kusi, Dominic Asoma

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.

Jay Bojič Burgos, Urban Sedlar, Matevž Pustišek

No abstract is available for this record.

Cherukupally, Rushil Lingaiah

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.

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.

Itay Tsabary, Alex Manuskin, Roi Bar-Zur, Ittay Eyal

No abstract is available for this record.

Yuxuan He, Chunqiang Hu, Bin Cai, Xiaoshuang Xing

No abstract is available for this record.