E-health systems have revolutionized healthcare by enabling efficient data sharing and management. However, they face significant security and privacy challenges, including unauthorized access, data breaches, identity fraud, and insurance fraud. Existing solutions attempt to address these issues but suffer from single points of failure, lack of patient-defined access control, and inadequate privacy-preserving mechanisms. This paper proposes a dual-blockchain architecture integrated with Self-Sovereign Identity and Zero-Knowledge Proofs to enhance security, privacy, and fraud resilience. The framework employs Decentralized Identifiers and Verifiable Credentials for secure authentication while leveraging the InterPlanetary File System for decentralized Electronic Health Records storage. By addressing the limitations of current systems, the proposed solution ensures a more secure, scalable, and privacy-preserving e-health environment.
Cryptocurrencies, particularly Bitcoin, continue to be the most prevalent use case within the blockchain ecosystem. One of the inherent limitations of blockchain is that it can create a false sense of privacy. All transaction history and the amount of cryptocurrency held are publicly available, and this information can be easily associated with specific individuals. Many works have proposed fully-private solutions, which are ideal but not realistic in many scenarios. This paper proposes a technical solution that enables private Bitcoin payments by default, but with the option to conditionally disclose payment data. To do so, this solution relies on unlinkability by a decentralized mixer, which can be reversed by a conditional discloser using a trapdoor unlinkability function. The conditional discloser, which also provides accountability of requests, obeys the payer's policies regarding who can access payment data. To ensure compliance, we propose a mixer that does not learn anything about the payment link, but is guaranteed by Zero-Knowledge Proofs that the payment can be relinked by a specific conditional discloser. Furthermore, we provide a proof-of-concept implementation of the proofs, using Circom and SnarkJS. We also present a benchmark that demonstrates the feasibility of this solution. It incurs only one additional parameter per on-chain transaction, while the remainder of the verification data is managed off-chain.
Open access
Blockchain Technology Applications and Security
Cryptography and Data Security
Advanced Steganography and Watermarking Techniques
Blockchain technology, renowned for its decentralized and secure nature, has gained substantial attention. Central to its functionality are consensus mechanisms, which are essential for validating transactions and upholding the integrity of the distributed ledger. However, the efficiency and scalability of blockchain are currently impeded by the resource limitations and excessive communication demands of existing consensus mechanisms. To address these challenges, we propose GT-BFT, a streamlined and lightweight blockchain consensus mechanism grounded in a global trust model. This model capitalizes on node behavior to form consensus groups and facilitate consensus achievement. GT-BFT integrates a novel approach of selective broadcasting along with a Byzantine threshold determination algorithm, significantly boosting both the efficiency and security of the network. Our extensive analysis and performance evaluation reveal that GT-BFT surpasses existing mechanisms in key areas such as security, system throughput, and transaction confirmation speed, marking a significant advancement in blockchain consensus technology.
Shahida Hafeezan Qureshi, Saif Ur Rehman Malik, Junaid Haseeb, Syed Atif Moqurrab · 6 authors
ABSTRACT Federated Learning (FL) is emerging as a premier paradigm for privacyâpreserved Machine Learning (ML), enabling devices to train models without central data pooling collaboratively. In the contemporary Internet of Things (IoT) landscape, characterized by escalating energy consumption and associated carbon footprint, FL is recognized not merely for its privacy features. Intrinsic to decentralized architectures such as FL, secure communication is based on digital signatures to guarantee integrity. This is particularly evident in sensitive sectors such as the Internet of Vehicles (IoV), banking, and healthcare. Integrating FL becomes imperative and intricate as these sectors are intertwined with the IoT fabric. Our study unveils âSecure Federated Learning Framework (SecFL),â a pioneering decentralized framework combining FL and sustainable computing. SecFL offers defences against adversarial attacks such as data poisoning and label flipping. Utilizing the RivestâShamirâAdleman (RSA) asymmetric encryption algorithm for securing digital communications and transactions, combined with ElGamal encryption and a private Ethereum blockchain, ensures enhanced clientâspecific security. Our research emphasizes the formal modeling of adversarial dynamics using HighâLevel Petri nets (HLPN) within the FLâIoT ecosystem, balancing system dynamics and energy conservation. Our model consistently outperforms contemporary solutions in accuracy and time efficiency after validation. As IoT burgeons into domains like environmental monitoring, smart cities, and energy grids, the SecFL framework, fostering FL, optimizes energy utilization and bolsters resource efficiency. In our comparative analysis, the Elliptic Curve Digital Signature Algorithm (ECDSA) algorithm demonstrates superior transaction latency and verification time compared to RSA and Elliptic Curve Cryptography (ECC).
The Internet of Things (IoT) has become an integral part of daily life, making the protection of user privacy increasingly important. In gateway-based IoT systems, user data is transmitted through gateways to platforms, pushing the data to various applications, widely used in smart cities, industrial IoT, smart farms, healthcare IoT, and other fields. Threshold Public Key Encryption (TPKE) provides a method to distribute private keys for decryption, enabling joint decryption by multiple parties, thus ensuring data security during gateway transmission, platform storage, and application access. However, existing TPKE schemes face several limitations, including vulnerability to quantum attacks, failure to meet Simulation-Security (SS) requirements, lack of verifiability, and inefficiency, which results in gateway-based IoT systems still being not secure and efficient enough. To address these challenges, we propose a Verifiable Simulation-Secure Threshold PKE scheme based on standard Module-LWE (VSSTPM). Our scheme resists quantum attacks, achieves SS, and incorporates Non-Interactive Zero-Knowledge (NIZK) proofs. Implementation and performance evaluations demonstrate that VSSTPM offers 112-bit quantum security and outperforms existing TPKE schemes in terms of efficiency. Compared to the ECC-based TPKE scheme, our scheme reduces the time cost for decryption participants by 72.66%, and the decryption verification of their scheme is 11 times slower than ours. Compared with the latest lattice-based TPKE scheme, our scheme reduces the time overhead by 90% and 48.9% in system user encryption and decryption verification, respectively, and their scheme is 13 times slower than ours in terms of decryption participants.
With the advancement of blockchain technology, smart contracts are increasingly applied in finance, supply chain, healthcare, and other domains. However, the demand for multi-party data joint analysis within these contracts faces challenges of privacy leakage and malicious deception. This paper applies secure multi-party computation (MPC) to smart contracts, based on the secure computation of intersection, union, and difference of multi-party multisets (IUDMM), to protect data privacy while supporting joint analysis. Existing multiset computation protocols are primarily designed for two-party scenarios under the semi-honest model, which are unsuitable for applications involving multiple participants in smart contracts. This paper introduces a protocol for IUDMM under the semi-honest model, utilizing the multi-key NTRU encryption algorithm and a novel vector encoding method. Furthermore, to address potential malicious behaviors, an IUDMM protocol under the malicious model is designed by incorporating digital commitment method. The protocolâs correctness is analyzed, and its security is proven using the real/ideal model paradigm. Additionally, it also resists collusion attacks by any party. Finally, efficiency analysis and experimental simulations demonstrate that the proposed protocols are efficient, reliable, and fair, providing a secure and practical solution for multi-party data joint analysis and privacy protection in smart contracts.
In this framework, Blockchain-Integrated Access Control for Wireless Edge Networks intends to attempt authentication and authorization by using smart contracts and immutable ledgers making it secure and decentralized. It increases trust among edge nodes by connecting them, thereby creating a single point of failure, while providing transparent and tamper-resistant enforcement of policies, which improves scalability, resilience, and performance, ultimately making it the Mold for IoT and edge computing environments. The objectives that the system intends to apply towards are design and implement decentralized access control for wireless edge networks using Blockchain, to provide tamper-proof identity verification solutions, to ensure dynamic access policies enforced through smart contracts, to reduce dependency on central authorities, and also to increase security and privacy, scaling trust, and transparency in the distributed IoT and edge environments. The proposed system proposed to implement decentralized access control via private Blockchain in wireless edge networks. Smart contracts are crafted to dynamically facilitate identity authentication, access rights, and the enforcement of policies. Edge nodes interface with the Blockchain to verify credentials and log access attempts immutably. To curb latency and overhead, lightweight cryptography schemes and consensus algorithms such as PBFT are employed. Simulation in a wireless edge environment showed improvements in access request validation by 35%, unauthorized access attempts down by 42%, and improved scalability with respect to conventional centralized models, showing that the model is effectual and robust in secure access control.
Zhi Lu, Mengyuan Zou, Samir M. Umran, Yuhao Long · 7 authors
Federated learning, as an emerging distributed machine learning approach, enables collaborative model training while protecting data privacy. However, federated learning is vulnerable to Byzantine attacks and inference attacks. Existing solutions typically require semi-honest servers to perform secure aggregation or lack effective input validation mechanisms. To address these issues, we propose RIDE, a secure aggregation protocol for decentralized federated learning with input validation. RIDE utilizes pedersen commitments and efficient zero-knowledge proofs to verify whether model updates comply with predefined constraints, ensuring client input privacy and integrity. Additionally, RIDE employs a publicly verifiable secret sharing scheme, ensuring that only validated model updates are aggregated, even in the presence of malicious clients or client dropouts. Experimental results on four real datasets demonstrate the effectiveness of our solution. For example, RIDE has a maximum bandwidth overhead of 7.11MB, which is only 1.31Ă that of the most popular secure aggregation protocol (CCS 2020), and the computational cost of RIDEâs execution on the CIFAR-10 L dataset is 109.88s, which is 7.28Ă faster than the current state-of-the-art protocol RoFL (S&P 2023).
Federated learning has found extensive application in the multimedia domain. However, due to its distributed nature, it is vulnerable to attacks such as Byzantine poisoning. To counteract malicious attacks, the secure aggregation process in federated learning requires input validation from participants. Existing input verification schemes, such as ACORN (USENIX Security 2023), ROFL (S&P 2023), et al., efficiently assess the validity of client inputs, but they fail to account for the impact of weights and do not support weighted secure aggregation. To address these issues, we propose α-SAV, an efficient weighted input verification scheme that utilizes Pedersen commitments to encrypt both privacy and weighted gradients. Our scheme incorporates a non-interactive zero-knowledge proof, the Sigma protocol, allowing clients to generate input proofs without interacting with the server. Verified inputs can then contribute to weighted aggregation. α-SAV is highly compatible, seamlessly integrating into existing federated learning frameworks with minimal additional cost. Experimental results demonstrate that the cost of α-SAV is linear. When trained on the MNIST dataset, the client computation time for α-SAV is 1.6 seconds, resulting in only 24% additional cost compared to ACORN and 3% compared to ROFL.
Abstract The increasing levels of data collection and processing, and the consequential risks to both individual users and society, have led to rising demands for privacy. This chapter first discuses different notions of privacy, including anonymity, and how cryptography relates to them. It then reviews a range of cryptographic privacy-enhancing technologies that can be used to support a variety of privacy objectives. The first is Tor, which is used to support anonymity on the internet. Next, it is shown how zero-knowledge proofs enable truths to be communicated without revealing unnecessary information. The goal of secure multiparty computation (MPC) is to enable sensitive data from multiple sources to be input to a computation while maintaining the privacy of the component data items. Finally, a range of further technologies are introduced, including blind signature schemes, group signature schemes, homomorphic encryption, private set intersection and private information retrieval.
While Federated learning (FL) is considered privacy-preserving by nature, it remains vulnerable to many attacks, such as data and model poisoning, that compromise data integrity and model accuracy. Conventional privacy-preserving federated learning (PPFL) mechanisms, including homomorphic encryption (HE), secure aggregation, and secure multiparty computation (SMPC) demonstrate several limitations, such as high computational complexity, significant communication overhead, and scalability challenges. To overcome the aforementioned issues, we propose an end-to-end secure FL architecture that integrates differential privacy (DP), zero-knowledge proof (ZKP), and median aggregation. DP prevents data leakage during model updates by introducing Laplacian noise for privacy preservation. ZKP is implemented through Schnorrâs protocol, which enables lightweight and efficient client authentication without revealing sensitive information. Finally, median aggregation is incorporated to mitigate the impact of outliers and adversarial updates, ensuring robust prediction aggregation. The experimental results indicate that the proposed approach outperforms other well-known PPFL methods including partially homomorphic encryption (PHE), fully homomorphic encryption (FHE) and SMPC. It delivers substantial improvements in global accuracy, especially for larger client counts, with gains of 10%-30% over the other methods. The client training time is significantly reduced by 70%-90%, ensuring faster processing. The approach also excels at reducing average round latency by 80%-95%, enhancing the overall efficiency of the system. Communication overhead is significantly reduced by 65%-85%, lowering data transfer costs per round. Furthermore, the size of the model is minimized by 60%-85%, making it more resource efficient and scalable for larger deployments.
This work proposes a Fiat-Shamir linkable ring lattice-based signature scheme, building on Yuen et al. âs generic ring signature. It converts the MLWE-based ÎŁ-protocol into a linkable ring signature through the Fiat-Shamir transformation, employing rejection sampling to avoid private key leakage. Linkability is achieved by attaching a public key structure label, avoiding complex zero-knowledge proofs and pseudorandom function calculations. Compared to other lattice-based schemes, our approach improves signature length. Security analysis under the random oracle model confirms correctness, anonymity, unforgeability, linkability, and non-slanderability.
Abstract The breaches of the blockchain wallet keys greatly harm the security of blockchain transactions. To protect the secret keys, the known solutions, such as hierarchical deterministic wallets proposed in BIP32 or stealth addresses adopted in Monero, have been extensively researched. However, most of the existing works assume the key is safe, in the sense that it cannot be stolen or damaged, which is not true in practice. Moreover, current key revocation mechanisms either rely on centralized authorities, compromising decentralization, or require economic incentives to ensure nodes remain consistantly online. In this paper, we introduce Cocoon, the first blockchain wallet scheme that supports stealth addresses and provides a wallet revocation mechanism without the need for certificates. Cocoon not only ensures the privacy of wallet secret keys but also can individually revoke compromised keys with high performance. Our contributions are three-fold: First, we present the formal model and the related security definitions. Next, we give a generic construction based on the hierarchical identity-based signature, identity-based key encapsulation mechanism and non-interactive zero-knowledge proof. We then extend the scheme to the hierarchical setting for diverse scenarios. Finally, we give the implementation, and the results show that the scheme is practical.
This paper introduces a comprehensive architectural framework for quantum-resistant health data management. The proposed Immutable Health Ledger (IHL) represents a fundamental paradigm shift an advanced Zero-Trust architecture engineered to withstand both current cyber threats and the emerging challenges posed by quantum computing, which are expected to render existing encryption standards obsolete. The IHL ensures provable data sovereignty through three foundational principles: a Biometric Trust Anchor in which patient identity serves as the cryptographic root of trust; a Post-Quantum Cryptographic Foundation built upon NIST-standardized algorithms with a hybrid deployment strategy; and a Distributed Integrity Layer that makes any form of data manipulation computationally and economically impractical. This document presents the complete mathematical formulations, formal security proofs, performance analyses, and an implementation roadmap that together define the operational and theoretical integrity of the proposed system.
Maintaining data authenticity, security, reliability, and integrity is challenging in decentralized storage and backup systems. Existing blockchain-based backup systems majorly depend on centralized off-chain storage or single-chain implementation, which leaves them vulnerable to security flaws, high expenses, and single points of failure. In this study, a multi-blockchain backup and restore system incorporates Zero-Knowledge Proofs (ZKPs) for data integrity verification and threshold cryptography for secure access control. Unlike traditional blockchain storage, this system distributes backup metadata across multiple blockchains (Ethereum, Hyperledger) while storing encrypted data in decentralized storage (IPFS). ZKPs provide integrity verification without revealing data, hence improving privacy and security. Also, Shamirâs Secret Sharing provides multi-party authentication for restoration, prohibiting unauthorized access. The system outperforms existing models by improving fault tolerance, privacy, and security compliance while reducing storage costs. Experimental evaluations compare the storage efficiency, retrieval speed, and security of this method against existing blockchain-based storage methods. This work proposes an innovative decentralized backup and restore system for managing security, efficiency, and redundancy, offering a robust solution for secure and verifiable data management in blockchain environments.
As democratic processes increasingly transition to digital environments, safeguarding voter privacy and maintaining electoral integrity have become paramount. This study investigates the application of Zero-Knowledge Proofs (ZKPs) as a cryptographic framework for developing secure and private electronic voting systems. A comparative performance evaluation was conducted between ZKP-based voting protocols and traditional systems, focusing on key metrics such as validation time, privacy leakage index, and memory usage. Quantitative data analysis, supported by statistical methods including mean comparisons and standard deviation assessments, highlights the superiority of ZKP-based systems in minimizing information leakage while maintaining verifiability. Although ZKP protocols introduce higher memory consumption, the trade-off results in substantially enhanced voter anonymity and reduced validation latency. The findings suggest that ZKPs provide a scalable and efficient solution to the dual challenge of transparency and privacy in digital voting infrastructures. This research contributes to the growing body of work on cryptographic voting technologies and underscores the importance of balancing security with performance in the design of future e-voting systems. Keywords: Zero-Knowledge Proofs, E-voting, Cryptography, Privacy, Secure Voting Systems, Digital Democracy, Voter Anonymity, Cryptographic Protocols, Electoral Integrity, Privacy-Preserving Computation
Bhatt Vinayak Vishwanath, Nishanth Kumar Pathi, Shinu Abhi
Zero Knowledge Proof (ZKP) is a cryptographic method that allows a prover to demonstrate to a verifier that a statement is true without revealing any additional information. This is highly relevant in healthcare, where data privacy is critical. By integrating ZKP into healthcare applications, sensitive patient data can be securely verified without exposing unnecessary information. In healthcare, protecting sensitive data while enabling seamless collaboration between doctors, laboratories, and other entities is crucial. Sharing or verifying data often leads to privacy risks. The project aims to develop a ZKP protocol for a healthcare system, allowing secure and private verification of sensitive medical data between doctors and laboratories. The ZKP protocol is implemented using a hybrid system with cloud services and local machines for secure computation. AWS Key Management Service (KMS) handles encryption and decryption. The protocol creates cryptographic proof for verifiers to confirm data validity without accessing the data itself. Established cryptographic libraries and healthcare test data are used in the implementation. The system ensures scalability and security by leveraging AWS infrastructure and MySQL database integration for proof storage. The ZKP protocol successfully verified health care data without exposing sensitive information, reducing the risk of data breaches. This approach improves data security without compromising functionality, setting new privacy standards in healthcare data management.
Jun 25, 2025·2025 IEEE/ACIS 29th International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD)
The advent of quantum computing poses a significant threat to the cryptographic foundations of current electronic voting (e-voting) systems, which commonly rely on algorithms such as Rivest-Shamir-Adleman (RSA) and Elliptic Curve Cryptography (ECC). These algorithms are believed to be vulnerable to attacks imposed by quantum computers, jeopardizing core e-voting properties, including ballot secrecy, integrity, and auditability. To address these challenges, this work presents a quantum-resistant electronic-voting (e-voting) system, named post-quantum e-voting system (PQEVS) built entirely upon cryptographic primitives standardized by the National Institute of Standards and Technology (NIST) for post-quantum security. The proposed PQEVS utilizes Dilithium for secure voter authentication, Brakerski/Fan-Vercauteren (BFV)-based Fully Homomorphic Encryption (FHE) for privacy-preserving vote tallying, and Picnic-based Zero-Knowledge Proofs (ZKPs) to ensure vote validity without compromising voter anonymity. Designed for modularity and scalability, our PQEVS delivers enhanced security while achieving significant performance gains, reducing vote processing latency by 85% and supporting throughputs of up to 36,000 votes per second. These results highlight the practicality and robustness of post-quantum cryptography in securing large-scale electoral processes, setting a new benchmark for verifiable and future-proof e-voting systems.
Federated Learning (FL) offers an attractive framework for collaboratively training AI models while preserving data privacy. However, it also introduces challenges in verifying the integrity and authenticity of model updates across diverse clients. Zero-Knowledge Proofs (ZKP) provide a promising means to address these issues by verifying computations without revealing underlying data. Yet, global verification using ZKP remains computationally expensive and does not scale well. To overcome these limitations, we propose a novel approach grounded in two key principles: (a) partial verification, targeting carefully selected subsets of data, can effectively mitigate adversarial attacks; and (b) robust data verification is essential, ensuring not only the consistency of model parameters but also the authenticity of the underlying data. We highlight the potential operation of this partial verification system, discuss novel research directions, and outline strategies for a wider integration into FL architectures.
Nominative signatures allow us to indicate who can verify a signature, and they can be employed to construct a non-transferable signature verification system that prevents the signature verification by a third party in unexpected situations. For example, this system can prevent IOU/loan certificate verification in unexpected situations. However, nominative signatures themselves do not allow the verifier to check whether the funds will be transferred in the future or have been transferred.It would be desirable to verify the fact simultaneously when the system involves a certain money transfer such as cryptocurrencies/cryptoassets. In this paper, we propose a smart contract-based non-transferable signature verification system using nominative signatures. We pay attention to the fact that the invisibility, which is a security requirement to be held for nominative signatures, allows us to publish nominative signatures on the blockchain. Our system can verify whether a money transfer actually will take place, in addition to indicating who can verify a signature. We transform the Hanaoka-Schuldt nominative signature scheme (ACNS 2011, IEICE Trans. 2016) which is constructed over a symmetric pairing to a scheme constructed over an asymmetric pairing, and evaluate the gas cost when a smart contract runs the verification algorithm of the modified Hanaoka-Schuldt nominative signature scheme.