An anonymous credential (AC) system with partial disclosure allows users to prove possession of a credential issued by an issuer while selectively disclosing a subset of their attributes to a verifier in a privacy-preserving manner. In keyed-verification AC (KVAC) systems, the issuer and verifier share a secret key. Existing KVAC schemes rely on computationally expensive zero-knowledge proofs during credential presentation, with the presentation size growing linearly with the number of attributes. In this work, we propose two highly efficient KVAC constructions that eliminate the need for zero-knowledge proofs during the credential presentation and achieve constant-size presentations. Our first construction adapts the approach of Fuchsbauer, Hanser and Slamanig (JoC'19), which achieved constant-size credential presentation in a publicly verifiable setting using their proposed structure-preserving signatures on equivalence classes (SPS-EQ) and set commitment schemes, to the KVAC setting. We introduce structure-preserving message authentication codes on equivalence classes (SP-MAC-EQ) and designated-verifier set commitments (DVSC), resulting in a KVAC system with constant-size credentials (2 group elements) and presentations (5 group elements). To avoid the bilinear groups and pairing operations required by SP-MAC-EQ, our second construction uses a homomorphic MAC with a simplified DVSC. While this sacrifices constant-size credentials (n+2 group elements, where n is the number of attributes), it retains constant-size presentations (2 group elements) in a pairingless setting. We formally prove the security of both constructions and provide open-source implementation results demonstrating their practicality. We extensively benchmarked our KVAC protocols and, additionally, bechmarked the efficiency of our SP-MAC-EQ scheme against the original SPS-EQ scheme, showcasing significant performance improvements.
In this paper, we show for the first time it is practical to privately delegate proof generation of zkSNARKs to a single server for computations of up to 2^20 R1CS constraints. We achieve this by computing zkSNARK proof generation over homomorphic ciphertexts, an approach we call blind zkSNARKs. We formalize the concept of blind proofs, analyze their cryptographic properties and show that the resulting blind zkSNARKs remain sound when compiled using BCS compilation. Our work follows the framework proposed by Garg et al. (Crypto'24) and improves the instantiation presented by Aranha et al. (Asiacrypt'24), which implements only the FRI subprotocol. By delegating proof generation, we are able to reduce client computation time from 10 minutes to mere seconds, while server computation time remains limited to 20 minutes. We also propose a practical construction for vCOED supporting constraint sizes four orders of magnitude larger than the current state-of-the-art verifiable FHE-based approaches. These results are achieved by optimizing Fractal for the GBFV homomorphic encryption scheme, including a novel method for making homomorphic NTT evaluation packing-friendly by computing it in two dimensions. Furthermore, we make the proofs publicly verifiable by appending a zero-knowledge Proof of Decryption (PoD). We propose a new construction for PoDs optimized for low proof generation time, exploiting modulus and ring switching in GBFV and using the Schwartz-Zippel lemma for proof batching; these techniques might be of independent interest. Finally, we implement the latter protocol in C and report on execution time and proof sizes.
Bitcoin is one of the famous cryptocurrencies in the world. It is a permissionless blockchain, and all transactions are stored in a public decentralized ledger. In its security design, Bitcoin utilizes various cryptographic primitives, such as hash functions and signature schemes. In the current version of Bitcoin, the Elliptic Curve Digital Signature Algorithm (ECDSA) is employed, which is not considered post-quantum secure due to the Shor's algorithm. Since December 2016, the National Institute of Standards and Technology (NIST) initiated a process to standardize certain post-quantum cryptographic primitives, including key encapsulation mechanisms (KEMs), public key encryption (PKE), and digital signature schemes. Dilithium, a lattice-based digital signature scheme, emerged as one of the winners of this competition and is recently standardized as ML-DSA (FIPS 204). In this work, we analyze the potential replacement of the ECDSA signature, the current signature in Bitcoin, with Dilithium, which is a post-quantum digital signature. This replacement will have a significant impact on many protocols within the Bitcoin ecosystem. The ECDSA algorithms are not only utilized for transaction signing and verification but also in wallet management. Bitcoin operates on a pseudonymous system rather than complete anonymity. To enhance privacy protection, the Bitcoin community has adopted a special type of (hierarchical) deterministic wallet as outlined in Bitcoin Improvement Proposal 32 (BIP32). We have constructed deterministic wallets by first designing DilithiumRK, a signature scheme with rerandomizable keys from Dilithium. Subsequently, we conducted a thorough security analysis and successful implementation of DilithiumRK.
Leela V, Kanishka K, Kaviyasri S, Guru Dharshan K ¡ 6 authors
Protecting privacy, availability, and security of Electronic Health Records (EHRs) is of utmost importance in the modern digital health industry. Traditional centralized storage system has been under increasing security threats like illegal access, data tampering, cyber-attack and single point of failure. To address these problems, we present in this work a new blockchain-based system which combines Token-Based Authentication and Permissioned Pure Proof-of-Stake (PPoS) consensus model. The architecture authenticates the identity of participants using Decentralized Identifiers (DIDs) and generates cryptographic tokens providing limited access to EHRs that expire over time. These tokens, together with encrypted metadata and access logs are written on a permissioned block chain to provide transparency, traceability and finegrained access control. For data privacy concern, the real health record is off-chained saved with encryption by the ChaCha20-Poly1305 algorithm. The PPoS enforced on Algorand blockchain, provides an efficient, green consensus for the authorized healthcare nodes. It is compliant with HIPAA and GDPR, has excellent audit features and provides a highly available, emergency override access.
We introduce zkMaP (Zero-Knowledge Succinct Non-Interactive Matrix Multiplication Proofs), a novel non-interactive zero-knowledge proof system for verifying matrix multiplication with significant improvements in efficiency and scalability. Our protocol leverages KZG polynomial commitments and an innovative inner-product reduction technique to reduce the verification of n x n matrix multiplication to a single pairing equation, thereby enabling constant-time verification independent of the matrix size. In particular, zkMaP requires only two pairing operations and produces proofs as small as 320 bytes, yielding a 96 percent reduction in proof size compared to prior schemes. Furthermore, the prover's computational complexity follows the state-of-the-art at O(n^2), with experimental results demonstrating that proofs for 1024 x 1024 matrices can be generated in approximately 12.21 seconds, offering a 16.14x speedup over previous methods. Our implementation also exhibits better memory efficiency, using only 24.58 MB of prover-side RAM for 1024 x 1024 matrices, and supports scalable batch processing, achieving per-proof generation times of 46.79 milliseconds for 1024 instances while maintaining a constant verification time of 3.6 ms.
Blockchain has become a critical enabler of secure data sharing in domains such as healthcare, finance, and digital identity. However, its reliance on classical cryptographic schemes (e.g., RSA, ECDSA, SHA-256) makes current systems vulnerable to emerging quantum computing attacks, raising risks to data confidentiality, integrity, and long-term trust. This paper addresses this challenge by proposing a modular hybrid framework that integrates post-quantum cryptographic (PQC) techniques into blockchain-based personal data sharing. The framework combines lattice-based encryption for protecting off-chain data, hash-based signatures for smart contract authentication, and quantum-safe zero-knowledge proofs and trusted execution environments (TEEs) for privacy-preserving verification and secure key management. To ground this design, we conducted a systematic literature review of 35 studies published between 2018 and 2025, analyzing security, scalability, interoperability, regulatory alignment, and user autonomy. Findings reveal that only 5 out of 35 studies (14%) explicitly addressed quantum threats, with over 80% focusing on theoretical resilience without testing implementation constraints. Furthermore, 90% of proposals neglected smart contract compatibility, and only 8% (3/35) incorporated TEEs, underscoring implementation barriers in contract execution, secure key management, and performance integration. Prototype evaluation demonstrated that the framework sustained 1,500 TPS on Hyperledger Fabric, achieved a 75% reduction in storage bloat using IPFS, and supported GDPR-aligned workflows with 99.98% audit log completion and 95% successful erasure requests. Privacy was further strengthened through zk-STARK proofs, which reduced unauthorized access by 40%, while TEEs improved key management efficiency by ~28%. Although PQC introduced 5â12 seconds of latency, consent revocation was processed in under 2.1 seconds, highlighting both the feasibility and trade-offs of practical post-quantum deployment. This work demonstrates a clear pathway toward quantum-resilient blockchain infrastructures that safeguard personal data, comply with regulatory standards, and maintain user trust in the quantum era.
The industrial internet of things (IIoT) expanded fast as physical devices and systems were connected to the internet. However, this interconnectedness made IIoT systems vulnerable to hackers. Intrusion detection systems (IDSs) were put in place to detect and prevent such assaults. Nonetheless, attackers might circumvent IDSs by forging identities or interfering with recorded data. The article intended to improve IIoT security by achieving system confidentiality, integrity, availability, scalability, performance, and security. For IIoT security, the article developed a secure federated learning access control framework (SecureFLACF) linked with a blockchain-based IDS. SecureFLACF used blockchain to secure data collected by IDS, AES-256 encryption to secure stored data, zero-knowledge proof (ZKP) to validate user identities and manage data access, and a federated learning access control framework (FLACF) to train a machine learning model for intrusion detection. SecureFLACF developed as a viable solution for improving IIoT security, providing strong assurances for IDS data and access control using blockchainâs tamper-proof structure and AES-256 encryption. Furthermore, FLACFâs design allows private machine learning model training, ensuring data privacy as well as model fidelity. The frameworkâs usefulness was highlighted by its application in real-world circumstances, making it a cost-effective option for organisations of all sizes. This method not only strengthened IIoT systems against a wide range of cyber threats, but also stressed their dependability as a safeguard. SecureFLACF exhibited considerable promise for improving IIoT security across several dimensions by encapsulating practicability, cost-effectiveness, and dependability.
La tesi indaga come le primitive crittografiche sostengano sicurezza e integritĂ di Bitcoin, coniugando teoria e pratica. Si parte dai fondamenti (riservatezza, integritĂ , autenticazione, non ripudio) e dalle basi di complessitĂ computazionale che giustificano la âone-waynessâ degli algoritmi moderni. Vengono presentate cifratura simmetrica e asimmetrica, funzioni hash e lâalgoritmo SHA-256 (double hashing), con cenni alla minaccia quantistica e agli standard post-quantum in via di adozione. Sul piano applicativo si descrive lâarchitettura: blockchain come registro append-only, Merkle tree e Merkle root per verifiche efficienti, gestione di chiavi e indirizzi; firme digitali ECDSA e lâevoluzione SegWit/Taproot con Schnorr e MAST, che riducono malleabilitĂ e ingombro on-chain migliorando privacy ed efficienza. La sezione operativa tratta HD wallet (seed phrase, derivation paths) e schemi avanzati di firma a soglia, evidenziandone impatti su usabilitĂ e rischio. La sicurezza di rete è analizzata attraverso i principali vettori dâattacco (double spending, 51%, address poisoning), il ruolo degli incentivi economici del mining e il retarget della difficoltĂ che stabilizza il tempo di blocco. Per la privacy si distinguono pseudonimia e anonimato e si valutano tecniche on/off-chain: CoinJoin/PayJoin, Stonewall(x2), Dandelion++ e Lightning Network; si discutono anche Zero-Knowledge Proofs e Self-Sovereign Identity con DIDs/VCs e cornice eIDAS. Infine si affronta la scalabilitĂ : trilemma sicurezza-decentralizzazione-throughput, ottimizzazioni on-chain (SegWit) e soluzioni Layer-2 (Lightning, sidechain), insieme alla governance degli aggiornamenti tramite soft e hard fork. Conclusione: un modello modulare in cui il Layer 1 resta strato di regolamento sicuro, mentre Layer-2 e nuove primitive crittografiche abilitano efficienza, privacy e resilienza nel lungo periodo.
Current systems for exchanging medical records struggle with efficiency and privacy issues. While establishing the Electronic Medical Record Exchange Center (EEC) in 2012 was intended to alleviate these issues, its centralized structure has brought about new attack vectors, such as performance bottlenecks, single points of failure, and an absence of patient consent over their data. Methods: This paper describes a novel EMR Gateway system that uses blockchain technology to exchange electronic medical records electronically, overcome the limitations of current centralized systems for sharing EMR, and leverage decentralization to enhance resilience, data privacy, and patient autonomy. Our proposed system is built on two interconnected blockchains: a Decentralized Identity Blockchain (DID-Chain) based on Ethereum for managing user identities via smart contracts, and an Electronic Medical Record Blockchain (EMR-Chain) implemented on Hyperledger Fabric to handle medical record indexes and fine-grained access control. To address the dual requirements of cross-platform data exchange and patient privacy, the system was developed based on the Fast Healthcare Interoperability Resources (FHIR) standard, incorporating stringent de-identification protocols. Our system is built using the FHIR standard. Think of it as a common language that lets different healthcare systems talk to each other without confusion. Plus, we are very serious about patient privacy and remove all personal details from the data to keep it confidential. When we tested its performance, the system handled things well. It can take in about 40 transactions every second and pull out data faster, at around 49 per second. To give you some perspective, this is far more than what the average hospital in Taiwan dealt with back in 2018. This shows our system is very solid and more than ready to handle even bigger workloads in the future.
Existing regulatory frameworks for decentralized anonymous payments help combat illicit activities such as money laundering and terrorism financing. However, With the development of the European Union's General Data Protection Regulation (GDPR) and data sovereignty, Existing frameworks struggle to balance privacy with regulatory compliance, often compromising user autonomy and data sovereignty. To address this, we propose the Linkable Distributed Regulatory Tag (LDRT) scheme, which enables traceability without altering transaction structure by leveraging Paillier homomorphic encryption and secret sharing. Building upon this, we introduce Decentralized Anonymous Payment with Data Sovereignty(DAPDS), the first regulatory framework for decentralized anonymous payments that complies with data sovereignty. DAPDS supports both UTXO and account-based models and employs a distributed regulator group with incentives to prevent dishonest behavior. We define and analyze the security properties of both LDRT and DAPDS, proving anonymity, linkability, traceability, collusion resistance, data sovereignty, distributed regulation, and fairness of incentive mechanism. Our work provides a valuable reference for future regulatory framework paradigms for anonymous privacy-preserving traceable blockchain. Experimental results show that DAPDS only incurs an additional 34ms of time cost over ETH in transacation phase and an average of only 2.66s per tracing request in tracing phase.
Many of the new quantum-resistant digital signature algorithms being evaluated and standardized by NIST rely on the Fiat-Shamir transform for security. This transformation takes a zero-knowledge (or negligible-knowledge) proof of identity and a hash function, and produces an algorithm for a digital signature. Luckily, none of these concepts are very hard to explain! I will give quick introductions to zero-knowledge proofs and hash functions, followed by two examples of digital signature algorithms based on Fiat-Shamir which you could use in an undergraduate classroom. The first one involves a lot of audience participation, and gives opportunities for drawing and decoration if you are so inclined. The second has been fairly well-known since the early days of public-key cryptography, and is likely to at least remind you of systems you have seen before. We wrap up with an overview of how Fiat-Shamir is used in post-quantum cryptography and in blockchains.
Umna Iftikhar, Hafiz Muhammad Attaullah, Inam Ullah Khan, Muhammad Mansoor Alam ¡ 6 authors
Verification of a qualification, achievement, quality, or aspect of a personâs background is one of the biggest problems nowadays as we have seen many platforms where students can get fake credentials. Every organization must select professional and academically qualified employees to give quality service. As a result, corporations rely on academic certifications to confirm and measure their prospective employeesâ academic qualifications. On the other hand, these employers lack a standardized process for confirming the legitimacy of academic certificates or degrees. Because the present procedures for verifying educational certifications are time-consuming, exhausting, and costly, just a few employers verify certificates for prospective employees. This research examines the issues that are related to the smart verification of someoneâs credentials. To make the process of verifying digital credentials quicker, simpler, and more cost-effective, we suggest decentralized architecture. We present the prototype, design, and implementation of the proposed framework.
Healthcare data management in cloud environments faces critical challenges in balancing security, privacy, operational efficiency, and regulatory compliance, as traditional access control mechanisms are inadequate for addressing the complex, multi-tenant, and dynamic nature of modern cloud infrastructures handling sensitive Electronic Health Records (EHRs). This paper introduces a novel hybrid framework that integrates Attribute-Based Access Control (ABAC) with Ethereum blockchain technology to establish a comprehensive multi-layered security architecture for cloud-based healthcare systems, combining advanced cryptographic techniques including double encryption using AES and RSA algorithms, Shamir's Secret Sharing for key management, smart contracts for automated policy enforcement, and multi-party computation protocols to ensure privacy preservation without compromising system performance. Extensive experimental evaluation conducted using 4,412 anonymized clinical records with 12 distinct attributes demonstrates exceptional efficiency with average record insertion time of 1.90 ms, retrieval time of 0.14 ms, blockchain mining time of 0.01 seconds, and hybrid architecture throughput of 562.94 records/second with 1.78 ms latency, significantly outperforming traditional centralized systems (337.17 rec/s, 2.97 ms) and pure blockchain implementations (3.37 rec/s, 296.98 ms). Security assessments reveal 100% operational reliability, 97.3% policy violation detection accuracy, 89% reduction in unauthorized access attempts, and comprehensive regulatory compliance with HIPAA (97%), GDPR (92%), ISO 27001 (94%), and SOC 2 (89%) standards, while maintaining optimized system resource utilization with CPU usage at 35.6%, memory at 48.0%, and network I/O at 34.1%. The framework addresses key limitations of existing approaches by providing seamless integration across multiple cloud layers, eliminating single points of failure, ensuring data immutability through distributed consensus mechanisms, maintaining comprehensive audit trails, and offering practical implications for healthcare organizations seeking secure, scalable, and compliant data sharing mechanisms in cloud-native environments, thereby contributing to the advancement of secure healthcare informatics by bridging the gap between theoretical access control models and practical implementation requirements in distributed cloud infrastructures.
Advances in differential equations and control theory are reshaping how secure, efficient medical data-exchange systems are designed. In parallel, blockchain offers decentralized trust, cryptographic integrity, and auditable access control for healthcare networks. Yet the choice of storage and transmission architecture strongly affects scalability, latency, privacy, and cost. This work investigates how mathematical modeling via differential equations and modern control processes can be coupled with blockchain to strengthen security and interoperability across distributed healthcare systems. We comparatively examine three deployment models: (1) on-chain storage, (2) off-chain, cloud-backed storage with blockchain access control, and (3) local institutional storage integrated with federated learning. On-chain designs maximize transparency and tamper-resistance but incur substantial computation and storage overhead. Off-chain approaches improve scalability while retaining verifiable control through the ledger. Local storage with federated learning safeguards patient privacy by keeping raw data within institutions and sharing only encrypted updates or proofs on chain. Persistent challenges include storage bloat, network delays, heterogeneous regulations, and evolving attack surfaces. To address these issues, we outline optimization strategies grounded in system dynamics stability analysis, resource allocation, and control-oriented tuning to balance throughput, privacy, and reliability. The study synthesizes theoretical insights with implementation considerations, offering a unified perspective on building resilient, performant, and privacy-preserving medical data-exchange frameworks that leverage blockchain under mathematically principled control.
Mr. Aditya S. G., Mr. Ram Anil Ainkar, Prof. Ms. Pranalini Joshi
The current Know Your Customer (KYC) ecosystem is largely built on centralized systems, which are vulnerable to data breaches, incur high operational costs, and often require customers to repeat verification steps unnecessarily [1], [2]. Such centralized designs concentrate sensitive data in single repositories, creating âhoneypotsâ that conflict with modern data privacy standards like the General Data Protection Regulation (GDPR) [3], [4]. At the same time, the transparent nature of public Distributed Ledger Technology(DLT) presents challenges for maintaining privacy in financial transactions, giving rise to what is often called the âBlockchain-PrivacyParadoxâ [5]. This survey explores cutting-edge DLT-based solutions that integrate Self-Sovereign Identity (SSI) and Zero-KnowledgeProof (ZKP) techniques. Key challenges in current approaches include scalability limitations in certain permissioned blockchains [6],inadequate mechanisms to fully support GDPRâs Right to Erasure [3], [4], [7], and the absence of reliable protocols to ensure legal access for Anti-Money Laundering (AML) compliance when users are uncooperative [8], [9].
Privacy-preserving technologies have introduced a paradigm shift that allows for realizable secure computing in real-world systems. The significant barrier to the practical adoption of these primitives is the computational and communication overhead that is incurred when applied at scale. In this paper, we present an overview of our efforts to bridge the gap between this overhead and practicality for privacy-preserving learning systems using multi-party computation (MPC), zero-knowledge proofs (ZKPs), and fully homomorphic encryption (FHE). Through meticulous hardware/software/algorithm co-design, we show progress towards enabling LLM-scale applications in privacy-preserving settings. We demonstrate the efficacy of our solutions in several contexts, including DNN IP ownership, ethical LLM usage enforcement, and transformer inference.
In the Internet of Vehicles (IoV) network, numerous vehicle terminals are required to continuously upload local data to maintain the latest service models, which supports intelligent transportation and personalized services. However, the privacy risks posed by this continuous data uploading cannot be ignored. Federated learning, as a distributed ma-chine learning paradigm, enables global model training without sharing original data. The introduction of blockchain further supports decentralization and immutability. However, federated learning also faces the risk of poisoning attacks, where malicious clients may upload abnormal or tampered model updates, severely impacting global model performance. To address this, this paper proposes a security framework that combines zero-knowledge proofs, federated learning, and blockchain. Clients use zero-knowledge proofs to ensure the legitimacy of uploaded updates, while the blockchain is responsible for verification and storage. Ultimately, a robust global model is obtained through federated aggregation. Experimental results demonstrate that this scheme effectively resists poisoning attacks, significantly improving system security and reliability while protecting user privacy.
Blockchain technology presents a transformative solution to the vulnerabilities inherent in traditional electoral processes by ensuring security, transparency, and scalability. By leveraging a decentralized and immutable ledger, blockchain provides vote integrity, voter anonymity, and end-to-end verifiability. This paper details the design, implementation, and evaluation of a blockchain-based online voting system using Ethereum smart contracts. The system integrates sophisticated cryptographic techniques, including zero-knowledge proofs and homomorphic encryption, alongside a distributed architecture to enhance election integrity. Our implementation demonstrates several key advantages: tamper-proof voting records, real-time verifiability of election progress, and scalability for large electorates. Performance analysis reveals an 85% reduction in vote counting time compared to traditional methods, with 99.99% accuracy and zero security breaches during testing. The system achieves cost efficiency through automated vote tallying while maintaining voter privacy. While challenges such as regulatory compliance and user adoption persist, this work demonstrates that blockchain technology holds the potential to revolutionize democratic processes by offering a secure, transparent, and accessible voting solution for the digital age.
The deployment of distributed digital twin systems in sectors such as healthcare, manufacturing, and critical infrastructure has significantly heightened the importance of data privacy. These systems interact with numerous devices and users, increasing the risk of data leakage or unauthorized access to sensitive information. Traditional centralized identity management and access control mechanisms no longer meet the scalability, autonomy, and privacy requirements of modern distributed architectures. This article explores how smart contracts operating in blockchain environments can provide decentralized access management for digital twin systems. Smart contracts enable transparent and reliable enforcement of access policies without relying on centralized authorities. The study examines the integration of modern cryptographic technologies into smart contract workflows, including zero-knowledge proofs, decentralized identifiers (DIDs), and confidential computing. These technologies make it possible to verify access rights and perform secure operations without revealing sensitive data. The article also analyzes the limitations of existing solutions, such as the high transaction costs of public blockchains, the limited performance of traditional smart contracts, and the challenges of integrating confidential computing into resource-constrained devices. The authors outline future research directions, including optimizing Layer 2 architectures to improve performance, developing secure auditing mechanisms, and ensuring compatibility with self-sovereign identity systems. The conclusions emphasize that privacy should be treated as a fundamental property of digital twin systems. In these environments, smart contracts must serve not only as governance logic but also as trusted agents that guarantee compliance with access policies and regulatory requirements in decentralized ecosystems.
Zero-Knowledge Proofs (ZKPs) enable users to prove knowledge of certain information without disclosing the information itself. Multi-ID ZKP systems extend this concept, allowing individuals to submit anonymous yet verifiable com plaints across various platforms while maintaining privacy and accountability. This paper explores the integration of ZKPs into complaint management systems, addressing the challenges of anonymity, verifiability, scalability, and computational efficiency. By reviewing existing literature on ZKP applications in authentication, identity management, and scalable systems, we identify key advancements and research gaps. This work aims to establish a foundation for implementing robust, privacy preserving, and efficient complaint systems leveraging multi-ID ZXP mechanisms.