The Ethereum blockchain has transformed decentralized finance (DeFi) and is widely used to issue ERC20 tokens. However, many of these tokens rely on unverified smart contracts, which pose serious security risks. Hackers can take advantage of vulnerabilities in these unverified ERC20 tokens, leading to scams, financial losses, and a decline in user trust. Although several tools are available to audit smart contracts, their effectiveness in analyzing unverified ERC20 tokens remains uncertain. This study examines three auditing tools HoneyBadger, Maian, and Mythril by testing how well they detect security issues in unverified ERC20 tokens. The SmartBugs framework was used to support the auditing process, enabling parallel execution, standardized reports, and bulk auditing of contracts. For a thorough evaluation, two datasets were used: one from 50,581 Ethereum blockchain blocks and another from the DappRadar list of blacklisted ERC20 tokens. These datasets were chosen to provide a broad and realistic view of how the tools perform on both typical and high-risk contracts. The tools were compared based on their ability to detect issues, their execution speed, and their overall effectiveness. The results revealed clear differences in performance: some tools were better at finding vulnerabilities accurately, while others focused more on speed than depth. This study emphasizes the need to improve smart contract auditing methods and highlights the importance of developing more effective security tools to strengthen the Ethereum blockchain.
S. Aarthi, K. Aravinthan, R. N. Ravikumar, N. Sivakumar · 5 authors
Data protection relies on cryptography to secure data across Blockchain, IoE, and Federated Learning systems. Strong cryptographic methods ensure confidentiality, authenticity, and integrity, safeguarding evolving digital security needs. Key techniques include symmetric and asymmetric encryption, hash functions, digital signatures, and zero-knowledge proofs. Cryptography enables secure protocols like TLS, homomorphic encryption, and differential privacy while addressing quantum-resistant security challenges, ensuring robust digital privacy solutions.
Yalan Wang, Liqun Chen, Yangguang Tian, Long Meng · 5 authors
Abstract The World Wide Web Consortium (W3C) has established standards for decentralized identities (DIDs) and verifiable credentials (VCs). A DID serves as a unique identifier for an entity, while a VC validates specific attributes associated with the DID holder. To prove ownership of credentials, users generate verifiable presentations (VPs). To enhance privacy, the W3C standards advocate for randomizable signatures in VC creation and zero-knowledge proofs for VP generation. However, these standards face a significant limitation: they cannot effectively verify cross-domain credentials while maintaining anonymity. In this paper, we present Anonymous Verifiable Presentations with Extended Usability (AVPEU), a novel framework that addresses this limitation through the introduction of a notary system. At the technical core of AVPEU lies our proposed randomizable message-hiding signature scheme. We provide both a generic construction of AVPEU and specific implementations based on Boneh–Boyen–Shacham, Camenisch–Lysyanskaya, and Pointcheval–Sanders signature. Our experimental results demonstrate the feasibility of these schemes.
Yanqi Zhao, Minghong Sun, Min Xie, Xiaoyi Yang · 5 authors
As online application services evolve, an increasing number of users are opting for subscription-based or paywall models to access high-quality content. Anonymous counting tokens (ACTs), which regulate user access while protecting user privacy, are widely adopted in the online paywall model. However, the centralized server of ACT may lead to a single point of failure, thereby exposing users’ privacy. To address this challenge, in this paper, we propose threshold anonymous counting tokens with batch proofs (ThrACT) that balance privacy preservation and access count limitation for online paywalls. We define the system model for ThrACT and provide its concrete construction. We utilize the threshold Boneh-Boyen signature to facilitate distributed issuance of anonymous tokens and enable batch issuance. In addition, our ThrACT employs non-interactive zero-knowledge proofs to verify the label and token requests while allowing the correctness of multiple blind token shares to be validated simultaneously. We also prove that ThrACT satisfies unforgeable and unlinkable security properties. Finally, we evaluate the computational cost of our ThrACT and compare it with other schemes. The experiment result demonstrates that ThrACT not only supports distributed issuance, batch verification, and counting functionalities but also achieves computational overhead in milliseconds. In particular, when the threshold is set to (3,5), the token issuance time is approximately 9 milliseconds.
Recent advances in Vector Oblivious Linear Evaluation (VOLE) protocols have enabled constant-round, fast, and scalable (designated-verifier) zero-knowledge proofs, significantly reducing prover computational cost. Existing protocols, such as QuickSilver [CCS'21] and LPZKv2 [CCS'22], achieve efficiency with prover costs of 4 multiplications in the extension field per AND gate for Boolean circuits, with one multiplication requiring a O (k log k) -bit operation where k== 128 is the security parameter, and 3–4 field multiplications per multiplication gate for arithmetic circuits over a large field. We introduce JesseQ, a suite of two VOLE-based protocols: JQv1 and JQv2, which advance state of the art. JQv1 requires only 2 scalar multiplications in an extension field per AND gate for Boolean circuits, with one scalar needing a$O(\kappa)$bit operation, and 2 field multiplications per multiplication gate for arithmetic circuits over a large field. In terms of communication costs, JQv1 needs just 1 field element per gate. JQv2 further reduces communication costs by half at the cost of doubling the prover's computation. Experiments show that, compared to the current state of the art, both JQv1 and JQv2 achieve at least 3.9× improvement in the online phase for Boolean circuits. For large field circuits, JQv1 has a similar performance, while JQv2 offers a 1.3× improvement. Additionally, both JQv1 and JQv2 maintain the same communication cost as the current state of the art. No-tably, on the cheapest AWS instances, JQv1 can prove 9.2 tril-lion AND gates (or 5.8 trillion multiplication gates over a 61-bit field) for just one US dollar. JesseQ excels in applications like inner products, matrix multiplication, and lattice problems, delivering 40% – 200% performance improvements compared to QuickSilver. Additionally, JesseQ integrates seamlessly with the sublinear Batchman framework [CCS'23], enabling further efficiency gains for batched disjunctive statements.
Due to the absence of a trusted model parameter sharing mechanism, model tampering, malicious uploads, and data inconsistency pose significant risks within the Federated Learning (FL) architecture. To address these challenges, this paper proposes an automated Smart Contract-based Asynchronous Model Aggregation (SCAMA) method to enhance model security in decentralized FL frameworks. We design a trigger-based asynchronous model submission mechanism driven by smart contracts. These contracts incorporate a dynamic adjustment strategy that can initiate global aggregation in real time. Furthermore, a decentralized parameter validity verification rule is embedded within the smart contract to ensure that submitted model updates align with the global optimization objective, thereby mitigating risks such as malicious uploads and training drift. Building upon this, we introduce a trustless model parameter-sharing strategy based on on-chain storage. The global model update process is encoded into the smart contract and executed through multiparty verifiable storage on a distributed ledger. During model aggregation, the smart contract utilizes encrypted hashing to verify the consistency of submitted models, ensuring that the parameter versions received by each node are traceable, reliable, and tamper-proof. Experimental results demonstrate that our method offers significant improvements in computational efficiency and model convergence speed compared with traditional synchronous FL approaches and existing asynchronous strategies.
May 7, 2025·2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE)
Digital advancement is restructuring governance and electoral systems which significantly has increased the adoption of electronic voting (e-voting). E-voting has various advantages like being fast, easily accessible and being more transparent than ever. But it has some security concerns like manipulation, data breaches etc. One of the most lightening issues is the advancement of quantum computing. Various traditional protocols like the Elliptic Curve Cryptography (ECC) and RSA are prone towards quantum attack which causes e-voting to rely on classical techniques for authentication, confidentiality and integrity. To address these concerns, the concept of quantum-resistant decentralised voting mechanisms has emerged. These mechanisms integrate post-quantum cryptographic techniques to safeguard election integrity in a future where quantum computers may compromise existing cryptographic frameworks. Blockchain technology, homomorphic encryption and zero-knowledge proofs (ZKPs) collectively enhance security, verification process and voter privacy while mitigating the risks of quantum threats.
G. Sharmila, K. Neha, M. Kaviya, M. Juhe Sherin · 5 authors
Blockchain technology is a cutting-edge advancement in information technology. Bitcoin, as one of its initial uses, has attracted considerable attention as a cryptocurrency. Alongside Ethereum, which emphasizes blockchain-driven smart contracts, these technologies lie at the heart of modern cryptocurrency innovation. Off-chain transactions offer a scalable solution for blockchain networks, reducing congestion, lowering transaction fees, and improving processing efficiency without compromising decentralization. However, existing off-chain solutions often face security and flexibility challenges, particularly in environments with high latency and unstable connectivity. The proposed system leverages the Hardhat blockchain framework with Ethereum to enable secure peer-to-peer transactions from user wallets, ensuring seamless fund transfers even in offline conditions. Additionally, it integrates blockchain-based email functionality, allowing encrypted messages to be sent securely over a decentralized network, thereby enhancing data privacy and security. To further strengthen data integrity, the system incorporates the Inter Planetary File System (IPFS) for decentralized file storage, reducing reliance on centralized servers and minimizing data loss risks. By combining off-chain transactions, blockchain-based email, and IPFS storage, the system enhances efficiency, security, and reliability, offering a robust decentralized solution for financial transactions and secure communication. The data is distributed across all cryptocurrency users within the network. This ensures that when a user initiates a transaction, data mining processes are conducted.
Blockchain technology offers a powerful solution for securing and managing legal and academic documents by providing decentralization, transparency, and immutability. This research presents a blockchain-based framework for tamper-proof legal document and credential management aimed at improving the reliability and efficiency of traditional verification systems. In the proposed architecture, applicants submit their credentials, which are authenticated by issuing institutions and stored in the InterPlanetary File System (IPFS) for decentralized file management, while only cryptographic hashes are recorded on the blockchain to ensure integrity, reduce storage costs, and improve scalability. The system supports multiple consensus mechanisms, including Proof of Work, Proof of Stake, and Practical Byzantine Fault Tolerance, and experimental results demonstrate that Proof of Stake achieves the best balance between security and transaction efficiency. A prototype implementation achieved a transaction throughput of up to 1,000 transactions per second with an average confirmation time of 5 seconds, significantly reducing verification time and manual processing compared to conventional methods. Comparative analysis confirms that the proposed solution enhances security, minimizes fraud, and improves cost-effectiveness. The framework provides a strong foundation for future extensions such as cross-chain interoperability, AI-driven fraud detection, and mobile-based verification platforms
With the rise in need of Electronic Health Records (EHR), keeping confidentiality, integrity, and privacy intact of Patient Health Information (PHI) becomes of utmost importance, particularly while sharing via email which is inherently non-secure. The current work proposes a hybrid encryption approach on the basis of Advanced Encryption Standard (AES-256) with symmetric key cryptography and Elliptic Curve Cryptography (ECC) as asymmetric encryption. AES-256 is selected because it performs well to encrypt large datasets, and ECC provides secure key exchange and digital signature support with less computation overhead compared to RSA. The system addresses issues of key encryption, such as secure transmission of information, identification authentication, and digital signatures for message integrity.Besides AES and ECC, the system incorporates a Zero-Knowledge Proof (ZKP) protocol, enabling privacy-preserving identity authentication without exposing sensitive data. This is important in ensuring PHI protection because ZKP permits identity and message authenticity verification with private data left secure. The hybrid system is balanced between speed of encryption and security, such that it remains scalable and usable for real-time healthcare use cases. By integrating AES for quick data encryption, ECC for secure key management, and ZKP for added privacy, this solution provides an end-to-end solution for securely sending PHI through email. It successfully solves encryption issues while maintaining confidentiality, integrity, and privacy in healthcare communication without compromising efficiency. This encryption framework ensures compliance with major data privacy regulations including HIPAA and GDPR, making it applicable for real-world healthcare environments across both the U.S. and the EU.
Mobile payment systems have transformed financial transactions with unprecedented ease and accessibility. But the explosion in their numbers has been met with increasing security threats and issues of privacy. This paper puts forward a new blockchain-based paradigm for mobile payment systems that prioritizes privacy but retains high-security standards. This design leverages the essential capabilities of blockchain technology like decentralization, immutability, and cryptographic protection. It incorporates advanced privacy-protecting techniques such as zero-knowledge proofs and homomorphic encryption. The system design intended here is to mitigate common vulnerabilities present in centralized systems, reduce the risk of data breaches, and provide users with more control over their financial data. By comparative analysis and simulations, we show that our privacy-oriented blockchain solution strongly improves transaction security, minimizes fraud, and maintains user anonymity without sacrificing system performance or compliance with regulations. This work is a contribution to the ongoing work on developing more secure and privacy-aware financial technologies in a rapidly digitalizing economy.
Yi-Jing Liu, Long Zhang, Xiaoqian Li, Hongyang Du · 7 authors
Federated learning (FL) is integral to advancing edge intelligence by enabling collaborative machine learning. In FL-empowered edge networks, computing nodes first train local models and then send them to an or multiple aggregation node(s) for global model collaboration. However, the trustworthiness of both local and global models in conventional FL frameworks is compromised due to inadequate model security and transparency. Distributed ledger technique (DLT) can address this issue by leveraging multi-nodes trust capabilities to support distributed consensus. However, model training and consensus performance of DLT may significantly degrade due to instability and resource constraints of edge networks. Sharding technique provides an effective approach by dividing the ledger into smaller and manageable shards. In this paper, to improve model training and consensus performance, we propose a trusted FL framework by incorporating sharding DLT into FL frameworks. We construct a theoretical model to investigate the relationship between model training performance, consensus efficiency, and capacity of edge nodes regarding storage, computing and communications. Based on the theoretical model, we propose a trusted clustering scheme to aggregate local models. Numerical results show that our proposed scheme significantly improves network throughput for transmitting models while guaranteeing model learning performance in comparison with some classical baselines.
Blockchain technology introduces a new decentralized paradigm era avoiding the reliance on trusted third parties. It is a transparent and distributed ledger which is designed fundamentally for digital cryptocurrencies but has since been extended to various industries. However, its immutability obligates significant challenges including storing illicit contents, privacy regulations violations, and restricting data management flexibility. Therefore, redactable blockchain has emerged as a leading solution enabling controlled immutable contents amendment. Transaction-level redaction reinforced by fine-grained access control forms the cornerstone of the current redaction mechanisms. This redaction concept essentially depends on modifying mutable transactions governed by predefined access policies specified by the transaction owner. Modifiers equipped with necessary rewriting privileges and who fulfil the associated access policy are enabled to perform modifications. However, the existing redaction mechanisms infrastructures are inefficient. For instance, the chameleon hash ephemeral trapdoor (chet),
To solve the privacy leakage and identity island problems in cross-chain interaction, we propose an anti-quantum cross-chain identity authentication approach based on dynamic group signature (DGS-AQCCIDAA) for smart education. The relay-based cross-chain model promotes interconnection in heterogeneous consortium blockchains. DGS is used as the endorsement strategy for cross-chain identity authentication. Our approach can ensure quantum security under the learning with error (LWE) and inhomogeneous small integer solution (ISIS) assumptions, and it uses non-interactive zero-knowledge proof (NIZKP) to protect user identity privacy. Our scheme has low calculation overhead and provides anonymous cross-chain identity authentication in the smart education system.
The rapid growth of digital technologies has intensified concerns about data privacy and security. Blockchain technology, combined with advanced cryptographic methods, presents a promising solution to enhance digital privacy by enabling decentralized, transparent, and tamper-resistant systems. This article explores the foundational principles of blockchain and cryptography, evaluates their interplay in preserving digital privacy, and examines current challenges and future directions. Through comprehensive analysis, the study highlights how cryptographic protocols such as zero-knowledge proofs and homomorphic encryption can significantly augment privacy on blockchain platforms, while addressing inherent scalability and usability challenges.
Although differential privacy (DP) is widely regarded as the de facto standard for data privacy, its implementation remains vulnerable to unfaithful execution by servers, particularly in distributed settings. In such cases, servers may sample noise from incorrect distributions or generate correlated noise while appearing to follow established protocols. This work addresses these malicious behaviours in a distributed client-server-verifier setup, under Verifiable Distributed Differential Privacy (VDDP), a novel framework for the verifiable execution of distributed DP mechanisms. We systematically capture end-to-end security and privacy guarantees against potentially colluding adversarial behaviours of clients, servers, and verifiers by characterizing the connections and distinctions between VDDP and zero-knowledge proofs (ZKPs). We develop three novel and efficient instantiations of VDDP: (1) the Verifiable Distributed Discrete Laplace Mechanism (VDDLM), which achieves up to a 400,000x improvement in proof generation efficiency with only 0.1--0.2x error compared with the previous state-of-the-art verifiable differentially private mechanism and includes a tight privacy analysis that accounts for all additional privacy losses due to numerical imprecisions, applicable to other secure computation protocols for DP mechanisms based on cryptography; (2) the Verifiable Distributed Discrete Gaussian Mechanism (VDDGM), an extension of VDDLM that incurs limited overhead in real-world applications; and (3) an improved solution to Verifiable Randomized Response (VRR) under local DP, as a special case of VDDP, achieving up to a 5,000x reduction in communication costs and verifier overhead.
Since Diffie and Hellman's pioneering work on asymmetric cryptography in 1976, digital signature technology has evolved through three phases—theoretical foundation, standardization, and diversified innovation—emerging as a cornerstone of trust in digital societies. Theoretically, foundational frameworks were established by RSA, DSA, and Schnorr algorithms. Standardization efforts, including NIST DSS, ISO/IEC series, and national systems (e.g., China's SM2/SM9, Russia's GOST), fostered a multipolar ecosystem. Extended-attribution technologies (blind, group, and ring signatures) addressed privacy and scenario-specific demands. Current challenges, such as quantum computing threats and privacy-regulation trade-offs, drive advancements in post-quantum cryptography (lattice-based signatures, hash-based XMSS) and privacy-enhancing mechanisms (verifiably encrypted signatures, homomorphic signatures), guided by ISO/IEC redactable standards and NIST's post-quantum initiative. Moving forward, digital signatures will deepen capabilities in provable security, quantum resistance, and adaptive policy control, underpinning trust architectures for emerging ecosystems like Web3 and the metaverse.
Open access
Cryptography and Data Security
Digital and Cyber Forensics
Advanced Steganography and Watermarking Techniques
This study introduces a cutting-edge architecture developed for the NewbornTime project, which uses advanced AI to analyze video data at birth and during newborn resuscitation, with the aim of improving newborn care. The proposed architecture addresses the crucial issues of patient consent, data security, and investing trust in healthcare by integrating Ethereum blockchain with cloud computing. Our blockchain-based consent application simplifies patient consent's secure and transparent management. We explain the smart contract mechanisms and privacy measures employed, ensuring data protection while permitting controlled data sharing among authorized parties. This work demonstrates the potential of combining blockchain and cloud technologies in healthcare, emphasizing their role in maintaining data integrity, with implications for computer science and healthcare innovation.
Hala Saeed Omar, M. A. Elsisy, Tamer O. Diab, Wageda I. Elsobky
Smart contracts are blockchain-based algorithms that activate when specific conditions are fulfilled. They streamline the execution of agreements, allowing both parties to trust the outcome instantly without needing intermediaries or experiencing delays. To ensure secure and verified contract execution, cryptographic methods such as hash functions and digital signatures are used. Additionally, mathematical approaches like mathematical proofs and finite state machines are applied in designing and assessing smart contracts to guarantee their proper functionality. This paper explores the mathematical foundations of smart contracts, highlighting how they rely on mathematics to ensure immutability, security, and enforceability. A key technique behind their encryption methods is the pseudo-random number generator, which is based on chaotic maps. These chaotic maps generate highly random patterns depending on the initial seed value through complex mathematical operations. This work provides an overview of how chaotic maps are implemented in smart contracts. Additionally, the results obtained from these chaotic maps are presented showing that these maps achieve a high performance in digital signature algorithms.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
Julio López Fenner, Carlos Castillo-Muñoz, Francisco Escobar, Ana Bustamante-Mora · 5 authors
Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear O(N) communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.
Yanghe Pan, Zhou Su, Yuntao Wang, Han Liu · 6 authors
Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.
SYMBOLEO is a specification language used formalize legal contracts, analyze properties, and generate smart contracts for Hyperledger Fabric. However, this ecosystem presents challenges, particularly in securing access to contract elements and enabling interactions with Cyber-Physical Systems (CPS), including IoT devices. This thesis aims to develop and validate a tool for converting extended SYMBOLEO specifications into smart contracts, with a focus on security and privacy. The thesis proposes an architecture to integrate smart contracts, Complex Event Processing (CEP), a message broker, and blockchain (Hyperledger Fabric), facilitating CPS interaction. Additionally, a Role-Based Access Control (RBAC) model is proposed, extending SYMBOLEO with RBAC-inspired concepts, forming SYMBOLEOAC, to regulate access and support dynamic contract execution. Existing code generation tools are also extended to exploit SYMBOLEOAC and this new architecture.