Secure personal data sharing remains a critical challenge in decentralized systems due to concerns over privacy, compliance, and trust. This paper presents the formal verification of a Blockchain-Based Security Model (BSM) designed to address these challenges through a multi-layered architecture. The proposed model integrates Chaincode-as-a-Service (CCaaS) on Hyperledger Fabric to ensure modular, maintainable, and scalable execution of smart contracts. A Flask-based API serves as the secure gateway for data operations and identity management. Sensitive data is stored off-chain using InterPlanetary File System (IPFS), preserving decentralization while minimizing on-chain bloat. Access control is enforced using efficient cryptographic techniques, while Intel SGX (or simulated enclaves) safeguards secure data processing and decryption within trusted execution environments. To further enhance privacy guarantees, Zero-Knowledge Proofs (ZKPs) are optionally integrated to enable verifiable claims without disclosing raw data. For assurance of correctness and security, the BSM is formally modeled using the Dolev-Yao attacker model and verified through ProVerif, focusing on key security properties such as confidentiality, integrity, authentication, and accountability. The findings confirm that the proposed model satisfies stringent security goals and is robust against symbolic adversaries. This work contributes a verifiable and extensible framework for privacy-preserving data sharing in sectors such as healthcare, finance, and government. To the best of our knowledge, this is among the first works to formally verify a blockchain-based security model that simultaneously integrates modular chaincode execution (CCaaS), trusted hardware enclaves (Intel SGX), decentralized off-chain storage (IPFS), and optional Zero-Knowledge Proofs (ZKPs) with a unified framework for personal data sharing.
Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.
U Parthiban, Vandhana Devi Pannerselvam, Ashok Murugesan, Kumar Ramasamy
Women still face persistent financial inclusion, employment verification, and wage transparency issues to their economic empowerment.Conventional hiring and financial systems impose disproportionate limitations on women to access credit and fair wages.ShePowerChain is a blockchain platform that aims to address these problems with decentralized finance, smart contracts, and verifiable credentials.ShePowerChain's secure, transparent, and automated transactions not only streamline processes but, depending on modeled scenarios, help reduce wage payment delays by 25% and increase women-led microloan access by 40%.By enabling secure, transparent, and automated transactions, ShePowerChain disintermediates, enhances job verification, and enforces wage fairness.The platform uses zero-knowledge proofs for privacy, multi-factor authentication for security, and Layer 2 scaling solutions for efficiency.While the results were from simulations and comparisons, they were not due to full real world usage.Comparative studies point to its potential to improve financial access, close wage gaps, and establish trust in hiring processes.The design also considers a serious ethical risk of excluding women who have low digital literacy, suggesting digital skills training, and adaptive strategies for community.Despite obstacles related to regulatory implications, and lack of digital literacy, blockchain provides an opportunity to facilitate and scale sustainable solutions to gender inclusive economic empowerment.
Brugeres, Maxence, Languille, Victor, Kuznetsov, Petr, Zarfaoui, Hamza
We propose a decentralized asset-transfer system that enjoys full privacy: no party can learn the details of a transaction, except for its issuer and its recipient. Furthermore, the recipient is not aware of the sender’s identity. Our system does not rely on consensus or synchrony assumptions, and therefore, it is responsive, since it runs at the actual network speed. Under the hood, every transaction creates a consumable coin equipped with a non-interactive zero-knowledge proof (NIZK) that confirms that the issuer has sufficient funds without revealing any information about her identity, the recipient’s identity, or the payment amount. Moreover, we equip our system with a regulatory enforcement mechanism that can be used to regulate transfer limits or restrict specific addresses from sending or receiving funds, while preserving the system’s privacy guarantees. Finally, we report on PaxPay, our implementation of Fully Private Asset Transfer (FPAT) that uses the Gnark library for the NIZKs. In our benchmark, PaxPay exhibits better performance than earlier proposals that either ensure only partial privacy, require some kind of network synchrony or do not implement regulation features. Our system thus reconciles privacy, responsiveness, regulation enforcement and performance.
As the field of cybersecurity has experienced continual changes, up-to-date techniques have become increasingly necessary to analyze and defend against threats. Furthermore, the current methods consistently produce false alarms and sometimes completely miss real threats. This paper proposes an approach that integrates secure blockchain technology with data preprocessing, deep learning, and reinforcement learning to enhance threat detection and response capabilities. To secure the exchange of threat intelligence information, a safe blockchain network is used, which comprises Byzantine Fault Tolerance for high data integrity and Zero-Knowledge Proofs for access control. All relevant information is cleaned and standardized prior to analysis. Subsequently, graph convolutional neural networks with autoencoders are trained on large unlabeled sets of threat data to automatically label various types of threats, with the system employing fuzzy logic to rank and score possible threats. Furthermore, we implemented a feedback loop that incorporates reinforcement learning, thereby improving model performance over time according to guidance provided by cybersecurity specialists. The proposed system achieved high accuracy, precision, negative predictive value, and MCC, as well as notably low FPR and FNR values. The results establish that the proposed system is a reliable and effective measure for detecting cyberthreats.
The Internet of Medical Things (IoMT) is transforming healthcare by enabling devices to generate and share critical patient data. However, securely sharing this data across different healthcare entities remains a significant challenge due to concerns over privacy and security. Traditional solutions using Ciphertext Policy Attribute-Based Encryption (CP-ABE), Self-Sovereign Identity (SSI), and Zero-Knowledge Proofs (ZKPs) offer secure and anonymous data access, but they often fall short in scalability and integration, particularly in cross domain environments. To address these limitations, we introduce SSL-XIoMT, an optimized SSI and ZKP authentication framework within a consortium Hyperledger-based environment. This innovative system integrates SSI under advanced Zero-Knowledge Scalable Transparent Argument of Knowledge (ZK-STARK) and Plonk protocols within a consortium Hyperledger framework for privacy-preserving identity verification. We enhance identity privacy by integrating Multi-Party Computation (MPC), ensuring that identity credentials and ZKP proofs are securely shared and reconstructed without exposing sensitive information. Additionally, we optimize CP-ABE by offloading complex computations to fog nodes, which pre-compute attributes and logical operations. This approach significantly reduces computational overhead and enhances both privacy and efficiency. Our extensive analysis shows that SSL-XIoMT dramatically improves the performance of processing time for CP-ABE encryption and decryption compared to current methods. Moreover, our hybrid ZKPs based authentication approach outperforms the existing schemes regarding processing time and flexibility. The throughput test also demonstrates that SSL-XIoMT is practical for large scale cross-domain data sharing implementation.
Mosbah Alown, Mehmet Sabır Kiraz, Muhammed Ali Bingöl
Electronic voting (e-voting) systems have significantly improved the traditional voting process by addressing key concerns such as security, public acceptability, and convenience. However, these systems often face unique challenges, such as ensuring voter privacy and verifiability, preventing coercion and double voting, and maintaining scalability while protecting participant confidentiality. This study critically analyses and compares various e-voting schemes and technologies, evaluating their security features, verifiability mechanisms, and potential vulnerabilities. This paper reviews Direct Recording Electronic (DRE) voting, internet voting, and blockchain-based e-voting systems. In so doing, we provide an understanding of cryptographic primitives employed in e-voting systems and how they address specific characteristics and challenges associated with each voting scheme. Furthermore, we examine the applications proposed by previous studies in the context of these voting systems, assessing their strengths, limitations, and impact on democratic procedures. The cryptographic primitives reviewed include techniques like homomorphic encryption, blind signatures, and zero-knowledge proofs, which can enhance voter privacy, verifiability, and resistance to coercion and double voting.
The Hadamard product (also known as element-wise multiplication) is a fundamental operation in linear algebra, performed by multiplying corresponding elements of two matrices with the same dimensions. This operation plays a crucial role in various fields, including cryptography, where it enables efficient and parallelizable computations on large datasets—particularly in the design of cryptographic protocols such as zero-knowledge proofs. In this paper, we propose a transparent and efficient method for proving the Hadamard product between vectors that are independently committed in the groups G1and G2under a pairing operation e : G1×G2→ GT . For a vector of length n, the prover has a complexity ofOλ(n), while the proof size isOλ(logn). The verifier operates with a complexity ofOλ(logn), which includesO(logn) operations in GT and onlyO(1) pairing operations, making verification highly efficient. We prove the security of our scheme under the Symmetric External Diffie-Hellman (SXDH) assumption. Furthermore, we propose an aggregator for Groth16 (EUROCRYPT 2016) zk-SNARKs and a proof aggregation technique for the general case of the KZG polynomial commitment scheme (ASIACRYPT 2010), where all crs are distinct. Both applications do not require an additional trusted setup, support logarithmic-sized aggregated proofs, and significantly reduce the verifier’s pairing operations toO(1).
Increasing attention to digital identity and self-sovereign identity (SSI) is gaining momentum. SSI brings various benefits to natural persons, such as owning controls; conversely, digital identity systems in the real world require Sybil-resistance to comply with anti-money laundering (AML) and other needs. CanDID by Maram et al. proposed that decentralized digital identity systems may achieve Sybil-resistance and preserve privacy by utilizing multi-party computation (MPC), assuming a distributed committee of trusted nodes. Pass et al. proposed the formal abstraction of attested execution secure processors (AESPs) while equipping hardware-assisted security in mobile devices has become the norm. We first describe our proposal to utilize AESPs for building secure Sybil-resistant SSI systems, the architecture with a set of system protocols$\Pi ^{{\mathcal {G}}_{\mathtt {att}}}$, which brings drastic flexibility and efficiency compared to existing systems. In addition, we propose a novel scheme that enables users (holders) to request verifiers to verify their credentials without AESPs, and it further achieves unlinkability among credentials created for public verification. Our scheme introduces a simplified format for computed claims and commitment-based anonymous identifiers. We also describe a technique to utilize zero-knowledge membership proofs, in particular, “One-Out-of-Many Proofs”$\Sigma $-protocol by Groth and Kohlweiss, which can prove the existence of an expected credential without identifying it. Along with other techniques, such as utilizing the BBS+ signature scheme, we demonstrate how our scheme can achieve its goals with the extended anonymous and Sybil-resistant SSI system protocols$\Pi ^{{\mathcal {G}}_{\mathtt {att}}+}$. Entitling unlinkability among derived credentials in the anonymous Sybil-resistant SSI results in proper privacy preservation.
Leandro Loffi, Gerson Luiz Camillo, Cristiano Antonio de Souza, Carla Merkle Westphall · 5 authors
Digital evidence plays an increasingly crucial role in judicial proceedings due to the exponential growth in the creation, storage, and transmission of digital data. However, its inherent volatility and susceptibility to tampering necessitate robust mechanisms to ensure integrity and authenticity, making an effective chain of custody (CoC) a fundamental requirement. While state-of-the-art reviews identify various aspects, it is necessary to include the use of Self-Sovereign Identity (SSI) systems within the scope of research. To address this challenge, this article conducts a systematic review of the literature on the use of blockchain and SSI in managing the chain of custody of digital evidence. The review began with 9,178 studies, which, after a rigorous process applying inclusion and exclusion criteria, resulted in 39 studies directly related to the research topic. The study maps and reviews techniques, tools, methods, approaches, and security components for managing the chain of custody of digital evidence. The findings confirm the widespread adoption of blockchain for preserving digital evidence while indicating that SSI remains an emerging and underexplored concept in forensic applications. The results highlight the need for further research on off-chain storage mechanisms, privacy-preserving techniques such as Zero-Knowledge Proofs (ZKPs) to enhance security, auditability, and interoperability when combined with Verifiable Credentials (VCs). By mapping the current state of research, this study provides valuable insights into CoC, Blockchain, and SSI in forensic-based proposals, identifying research gaps, limitations, and opportunities for developing more robust and scalable evidence management systems.
Authentication is an important security issue for multi-access edge computing (MEC). To restrict user access from untrusted devices, Bring Your Own Device (BYOD) policy has been proposed to authenticate users and devices simultaneously. However, when integrating BYOD policy into MEC authentication to improve security, issues of efficient binding and user-device conditional anonymity have not been well supported. To address these issues, we propose Bring Your Device Group (BYDG) policy by constructing efficient and privacy-preserving user-device authentication. Our core idea is to use key sequences generated by PUFs-based key derivation functions (KDFs) to not only construct efficient binding relationships, but also achieve conditional anonymity for device groups. Specifically, a flexible and secure binding method is first developed by leveraging Chinese Remainder Theorem (CRT) to bind user with device groups. Each device’s CRT modulus is derived from the key sequence to construct many-to-many user-device binding relationships, which are managed in the form of on-chain Pedersen Commitment. Moreover, we design an identity anonymizing and tracing method for device groups. The key sequence is regarded as traceable device pseudo-identities, and then inserted into the cuckoo filter to reduce the on-chain storage overhead and mitigate malicious login attempts with low costs. Based on above two methods, the combination of Pedersen Commitment and Zero-Knowledge Proof of Knowledge is used to achieve user-device authentication with conditional anonymity. The security analysis was presented to demonstrate important security properties. A proof-of-concept prototype was implemented to conduct performance evaluation and comparative analysis.
The increasing reliance on e-commerce platforms has amplified challenges related to transparency, trust, fraud, and inefficiencies in reward distribution systems. Existing centralized architectures fail to address these issues effectively. This paper proposes BlockArc, a blockchain-based smart contract framework designed to revolutionize reward systems in e-commerce. The system leverages a permissioned blockchain and smart contracts to automate reward distribution, enhance security, and ensure transaction transparency. The framework consists of four layers: Blockchain Layer, Smart Contract Layer, Application Layer, and User Roles, each addressing key challenges such as reward fragmentation, fraudulent transactions, and inefficient refund processes. Smart contracts autonomously handle reward issuance, redemption, and expiration while integrating oracles for real-world data validation. Security is reinforced using cryptographic hashing, Zero-Knowledge Proofs (ZKPs), and Role-Based Access Control (RBAC) to prevent fraudulent activities. A performance evaluation demonstrated 112 transactions per second (TPS) under moderate load, fraud detection accuracy of 100%, and a 37% reduction in operational costs by eliminating intermediaries. User satisfaction surveys indicated high levels of trust and transparency. The study concludes that BlockArc enhances e-commerce reward systems by improving efficiency, security, and decentralization, paving the way for scalable and interoperable blockchain applications in digital commerce.
Blockchain technology has emerged as prominent player in supporting the United Nations’ Sustainable Development Goals (SDGs), by providing transparent, decentralized solutions across various sectors. However, the inherent transparency of blockchain raises significant privacy concerns, particularly in sensitive fields such as healthcare, finance, and supply chains, where confidentiality is essential. To address these challenges, we delve into emerging privacy-preserving techniques such as Ring Signatures, Zero-Knowledge Proofs (ZKPs), Secure Multi-Party Computation (SMPC), Trusted Execution Environments (TEEs), and mixers, examining their implementation, effectiveness and limitations in safeguarding user data without compromising blockchain’s core features. Additionally, we provide a deep technical analysis of next-generation blockchain platforms, which implement these techniques to balance privacy, scalability, and interoperability. By offering a deep dive into the architectures and privacy mechanisms of these platforms, this paper contributes to understanding how blockchain can be leveraged to achieve SDG objectives while addressing the critical issue of data privacy.
The advent of 6G networks places very high demands on ultra-low latency, high throughput, and quantum-secure communication to power Industry 5.0 use cases. Traditional blockchain architectures, given their decentralized and secure nature, often fall short in meeting the performance and security requirements of such an ecosystem. In this paper, we present a post-quantum blockchain architecture that employs CRYSTALS-Dilithium and SPHINCS+ for digital signatures and block and transaction verification, respectively, along with zk-STARKs to facilitate scalable zero-knowledge proof-based privacy, and a DPoS+VDFs consensus protocol to satisfy fairness and efficiency. We prototyped and evaluated the proposed framework with a benchmarking setup composed of Python, PQClean, liboqs, and Google Benchmark tools. Experimental results demonstrate that the system achieves a 40% reduction in latency, a 35% increase in transaction throughput, and a 25% reduction in computational overhead due to the integration of zk-STARK. Furthermore, finality time for consensus was reduced by 30% by using the hybrid DPoS-VDF consensus approach. Comparative studies with various lattice-, hash-, and code-based quantum cryptographic primitives have shown that CRYSTALS-Dilithium and SPHINCS+ outperform others in key generation, signing, and verification performance indicators, and thus qualify as optimal solutions for edge-centric 6G infrastructures. Conversely, zk-Starks showed near-optimal timeliness and verification effectiveness among the several examined zero-knowledge proof schemes. These findings validate the proposed framework as an efficient, scalable, and performance-enhanced blockchain solution for securing industrial ecosystems with latency sensitivity in a 6G-enabled environment.
Frontier AI systems, including large-scale machine learning models and autonomous decision-making technologies, are deployed across critical sectors such as finance, healthcare, and national security. These present new cyber-risks, including adversarial exploitation, data integrity threats, and legal ambiguities in accountability. The absence of a unified regulatory framework has led to inconsistencies in oversight, creating vulnerabilities that can be exploited at scale. By integrating perspectives from cybersecurity, legal studies, and computational risk assessment, this research evaluates regulatory strategies for addressing AI-specific threats, such as model inversion attacks, data poisoning, and adversarial manipulations that undermine system reliability. The methodology involves a comparative analysis of domestic and international AI policies, assessing their effectiveness in managing emerging threats. Additionally, the study explores the role of cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, in enhancing compliance, protecting sensitive data, and ensuring algorithmic accountability. Findings indicate that current regulatory efforts are fragmented and reactive, lacking the necessary provisions to address the evolving risks associated with frontier AI. The study advocates for a structured regulatory framework that integrates security-first governance models, proactive compliance mechanisms, and coordinated global oversight to mitigate AI-driven threats. The investigation considers that we do not live in a world where most countries seem to be wishing to follow European Union ideals, and in the wake of this particular trend, this research presents a regulatory blueprint that balances technological advancement with decentralised security enforcement.
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Ethics and Social Impacts of AI
Artificial Intelligence in Healthcare and Education
Succinct non-interactive zero-knowledge proofs (zk-SNARKs) are a powerful cryptographic primitive that allow a prover to convince a verifier of the truth of a statement without revealing any additional information. Due to the high computational cost associated with proof generation in existing zk-SNARKs, distributed zero-knowledge proving has emerged as a promising outsourcing approach, where the prover delegates heavy computation to multiple servers across different locations, as seen in systems like Siniel and zkSaaS. However, existing distributed zkSNARKs still rely on large prime fields, which increase computational overhead, and they often suffer from unavoidable network bandwidth bottlenecks. In this paper, we propose FDzkS, a flexible and efficient collaborative proving distributed protocol constructed using group signatures and binary fields. Our protocol allows the prover to delegate computation to multiple workers without revealing any part of the witness. Most importantly, compared with existing distributed zkSNARK schemes, FDzkS enables both the prover and the workers to perform their tasks almost entirely offline, and it avoids complex interactions among the workers. We benchmark FDzkS against the most advanced protocols such as Siniel, Eos, zkSaaS, and Pianist, covering both semi-honest and malicious worker settings. Experimental results show that under low bandwidth conditions (64 Mbps), FDzkS reduces total proving time by up to 300 under high bandwidth conditions (4 Gbps), it still achieves up to 200% improvement in efficiency.