Data marketplace are rapidly gaining traction as critical components of the modern data economy. However, traditional centralized marketplaces suffer from inherent challenges such as data leakage, lack of user control and single points of failure. To address these limitations, we propose Versatile Peer Network (VePran) – a decentralized data market place built on the Web3 suite of technologies. VePraN is designed to be modular, scalable and aligned with open standards, ensuring broad interoperability and future extensibility. Leveraging the InterPlanetary File System for persistent storage and blockchain for identity and ownership management, the platform offers a robust infrastructure that enhances data security and provenance. Unlike existing buyer centric solutions, VePraN adopts a seller oriented approach, empowering data owners with greater autonomy, fair exchange and control over their data assets. In addition to enabling secure data exchange, the platform facilitates the trading of machine learning models, expanding its utility in AI driven ecosystems. Verification mechanisms such as Merkle roots and Non- Fungible Tokens are employed to ensure data integrity and authenticity. This paper presents the architecture and implementation of VePraN as a foundational step toward a more equitable and resilient data exchange system.
Emanuela Podda, Pol Hölzmer, Alexandre Amard, Johannes Sedlmeir · 5 authors
Zero-knowledge proofs allow the implementation of the data minimisation principle imposed by the GDPR in digital identity wallets and the related personal data transactions, therefore representing a reasonable option to be enforced by lawmakers.
Carlos Beis-Penedo, Francisco Troncoso‐Pastoriza, Rebeca P. Dı́az Redondo, Ana Fernández Vilas · 6 authors
The rapid growth of Internet of Things (IoT) devices and applications has led to an increased demand for advanced analytics and machine learning techniques capable of handling the challenges associated with data privacy, security, and scalability. Federated learning (FL) and blockchain technologies have emerged as promising approaches to address these challenges by enabling decentralized, secure, and privacy-preserving model training on distributed data sources. In this paper, we present a novel IoT solution that combines the incremental learning vector quantization algorithm (XuILVQ) with Ethereum blockchain technology to facilitate secure and efficient data sharing, model training, and prototype storage in a distributed environment. Our proposed architecture addresses the shortcomings of existing blockchain-based FL solutions by reducing computational and communication overheads while maintaining data privacy and security. We assess the performance of our system through a series of experiments, showing its potential to enhance the accuracy and efficiency of machine learning tasks in IoT settings.
Mukkamalla Hari Naga Veni, M. Ashok Kumar, P.Ashok Reddy
The fast growth of the internet of vehicles (IoV), protecting data privacy and providing credible misbehavior detection have become major issues. Classical detection methods based on centralized servers are more prone to single points of failure, scalability bottlenecks, and privacy breaches. This project presents a privacy-preserving misbehavior detection system for IoV by combining federated learning (FL) and Blockchain technologies. In our solution, cars train machine learning models on their own driving data, and only model updates, instead of raw data are exchanged between the network, with user privacy ensured. The updates are hashed and stored securely on InterPlanetary FileSystem (IPFS) and registered on the Ethereum blockchain through smart contracts, making data immutable and transparent. The blockchain element, deployed through Web3.py and Ganache, ensures trustworthiness and tamper-proofing of misbehavior reports. Decentralized architecture, which does not only improve the robustness and security of the detection process but also avoids dependency on a central authority, making the system scalable and resilient.
Blockchain provides the opportunity for organizations to execute trustable collaborations through smart contract automations. However, linkability problems exist in blockchain-based collaboration platforms due to privacy leakages, which, when exploited, will result in tracing transaction patterns to users and exposing collaborating organizations and parties. Some privacy-preserving mechanisms have been adopted to reduce linkability problems through the integration of access control systems to smart contracts, off-chain data storage, usage of permissioned blockchain, etc. Still, linkability problems persist in applications deployed in both private and public blockchain networks. Zero-knowledge proof (ZKP) systems provide mechanisms for verifying the correctness of transactions and actions executed on the blockchain without revealing complete information about the transaction. Hence, ZKP systems provide a potential solution to eliminating linkability problems in blockchain-based collaboration systems. The objective of this paper is to identify various linkability problems that exist in blockchain-enabled collaboration systems and understand how ZKP algorithms and smart contract frameworks can be used in addressing the linkability problems. Furthermore, a proof of concept (PoC) is implemented and simulated to demonstrate a ZKP system for a privacy-preserving feedback mechanism that mitigates linkability problems in collaboration systems. The scenario-based results from the PoC evaluation show that a feedback system that includes project participants’ verification through membership proofs, verification of on-time submission of feedback through range proofs, and encrypted calculation of feedback scores through homomorphic arithmetic provides a privacy-aware system for executing collaborations on the blockchain without linking project participants.
The rapid digitization of e-government systems has introduced significant privacy challenges, including unauthorized data access and identity theft, which threaten the integrity and trustworthiness of public services. This study proposes a privacy protection framework based on Zero-Knowledge Proofs (ZKP), a cryptographic technique enabling secure verification without revealing sensitive information. The framework addresses critical privacy concerns such as secure identity verification, data confidentiality, and compliance with regulatory standards. By integrating advanced ZKP schemes, including Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs) and Bulletproofs, the framework ensures efficient proof generation and verification while minimizing computational overhead. A performance evaluation demonstrated that the proposed framework reduces privacy risks by 78% and achieves a threefold increase in transaction throughput compared to traditional cryptographic methods, such as Rivest–Shamir–Adleman (RSA) and Public Key Infrastructure (PKI). The scalability and efficiency of the framework were validated through extensive computational overhead analysis and comparative benchmarking. Additionally, trusted setup optimizations and constraint system modeling were employed to enhance the framework’s robustness and adaptability for large-scale e-government applications.
Reisha Ali, Akshat Gupta, Maria Francis, Kotaro Kataoka
Decentralized applications (DApps) over blockchains often require the user’s personal information for authentication. However, the public and transparent nature of blockchains can compromise user privacy. Threshold-based anonymous credentials (TAC) provide anonymous and unlinkable authentication, which helps preserve user privacy. Additionally, the design of TAC aligns well with blockchain’s decentralized nature because TAC offers decentralized trust distribution to prevent a single point of failure. However, only a few of them have implementations over blockchains because TAC requires computationally expensive cryptographic operations such as pairings and verification of zero-knowledge proofs (ZKPs) to be done on-chain. Thus, existing TAC systems have not been evaluated in a public permission-less blockchain environment. This evaluation is crucial to assess the efficiency and practicality of deploying TAC in real-world blockchain use cases to make TAC based DApps development feasible. This work presents the design and evaluation of three state-of-the-art TAC systems, RP-Coconut, threshold BBS+ (T-BBS+), and BBS (T-BBS), over the Ethereum blockchain. The evaluation compares the performance of these TAC systems on the Sepolia testnet in terms of execution time and gas usage. Additionally, this work also proposes partial credential verification mechanisms for T-BBS+ and T-BBS that significantly reduce the complexity of identifying valid credentials, thereby lowering the execution time at the user’s end. Furthermore, the implementation for blind issuance and associated ZKPs for T-BBS is provided, which was not previously detailed in the literature and is critical for its correct implementation.
As the demand for e-voting grows, it has become particularly important to ensure the security and fairness of the voting system. Therefore, an e-voting system is studied and designed to ensure the non-tamperability of voting results. The system utilizes zero-knowledge proof to verify the identity of the voter, while ensuring the tamperability of the voting data through blockchain technology. The experimental results indicated that the system outperformed the existing schemes in terms of processing speed and verification efficiency. Specifically, when the number of voters was 200, the time consuming and single verification time of this system were 3.7 s and 6.4 ms, respectively. When the number of voters increased to 600, the time consuming and single verification time were 8.3 s and 13.3 ms, respectively. In the number of candidates/voters was 5/80, none of the system’s gas consumption exceeded the maximum limit of a single transaction in Ether. Among them, the gas consumption of Vote Control contract was 5577485, and the gas consumption of non-interactive zero knowledge contract was 3826753. Furthermore, the more candidates there were, the longer it took the system to operate, although the number of voters had less of an effect on the cost of operating the voter system. The above outcomes reveal that the e-voting system proposed in the study provides a secure and efficient solution for small-scale voting activities and provides a basis for future optimization of large-scale voting scenarios.
The increasing adoption of Decentralized Applications (DApps) and Web3 infrastructures has exposed critical security challenges, including malicious smart contracts, fraudulent transactions, and decentralized governance exploits. Traditional threat intelligence systems rely on centralized security models, which create single points of failure, reduce data sovereignty, and limit real-time risk mitigation. To address these challenges, we introduce a Decentralized Federated Risk Analysis (DFRA) system, leveraging federated risk aggregation, decentralized storage, and automated security intelligence retrieval to enhance cybersecurity in DApps. Our DFRA system operates through three primary components: (1) Federated Risk Aggregation, where a federated model retrieves and consolidates risk scores, flagged threats, and security insights across decentralized sources; (2) MinIOBased Decentralized Storage, which stores security intelligence in an object storage system to allow distributed retrieval; and (3) Automated Security Intelligence Retrieval, a server-based process that periodically fetches and processes security data in real-time.
Xin Liu, Xinyuan Guo, Dan Luo, Liang Li · 9 authors
Federated learning promotes the development of cross-domain intelligent applications under the premise of protecting data privacy, but there are still problems of sensitive parameter information leakage of multi-party data temporal alignment and resource scheduling process, and traditional symmetric encryption schemes suffer from low efficiency and poor security. To this end, in this paper, based on the modified NTRU-type multi-key fully homomorphic encryption scheme, an asymmetric algorithm, a secure computation scheme of multi-party least common multiple and greatest common divisor without full set under the semi-honest model is proposed. Participants strictly follow the established process. Nevertheless, considering that malicious participants may engage in poisoning attacks such as tampering with or uploading incorrect data to disrupt the protocol process and cause incorrect results, a scheme against malicious spoofing is further proposed, which resists malicious spoofing behaviors and not all malicious attacks, to verify the correctness of input parameters or data through hash functions and zero-knowledge proof, ensuring it can run safely and stably. Experimental results show that our semi-honest model scheme improves the efficiency by 39.5% and 45.6% compared to similar schemes under different parameter conditions, and it is able to efficiently process small and medium-sized data in real time under high bandwidth; although there is an average time increase of 1.39 s, the anti-malicious spoofing scheme takes into account both security and efficiency, achieving the design expectations.
Secure Digital Evidence Management Systems (DEMSs) ae crucial for law enforcement agencies, because traditional systems are prone to tampering and unauthorised access. Blockchain technology, particularly private blockchains, offers a solution by providing a centralised and tamper-proof system. This study proposes a private blockchain using Proof of Work (PoW) to securely manage digital evidence. Miners are assigned specific nonce ranges to accelerate the mining process, called collaborative mining, to enhance the scalability challenges in DEMSs. Transaction data includes digital evidence to generate a Non-Fungible Token (NFT). Miners use NFTs to solve the puzzle according to the assigned difficulty level d, so as to generate a hash using SHA-256 and add it to the ledger. Users can verify the integrity and authenticity of records by re-generating the hash and comparing it with the one stored in the ledger. Our results show that the data was verified with 100% precision. The mining time was 2.5 s, and the nonce iterations were as high as 80×103 for d=5. This approach improves the scalability and integrity of digital evidence management by reducing the overall mining time.
Smart cities use advanced infrastructure and technology to improve the quality of life for their citizens. Collaborative services in smart cities are making the smart city ecosystem more reliable. These services are required to enhance the operation of interoperable systems, such as smart transportation services that share their data with smart safety services to execute emergency response, surveillance, and criminal prevention measures. However, an important issue in this ecosystem is data security, which involves the protection of sensitive data exchange during the interoperability of heterogeneous smart services. Researchers have addressed these issues through blockchain integration and the implementation of smart contracts, where collaborative applications can enhance both the efficiency and security of the smart city ecosystem. Despite these facts, complexity is an issue in smart contracts since complex coding associated with their deployment might influence the performance and scalability of collaborative applications in interconnected systems. These challenges underscore the need to optimize smart contract code to ensure efficient and scalable solutions in the smart city ecosystem. In this article, we propose a new framework that integrates generative AI with blockchain in order to eliminate the limitations of smart contracts. We make use of models such as GPT-2, GPT-3, and GPT4, which natively can write and optimize code in an efficient manner and support multiple programming languages, including Python 3.12.x and Solidity. To validate our proposed framework, we integrate these models with already existing frameworks for collaborative smart services to optimize smart contract code, reducing resource-intensive processes while maintaining security and efficiency. Our findings demonstrate that GPT-4-based optimized smart contracts outperform other optimized and non-optimized approaches. This integration reduces smart contract execution overhead, enhances security, and improves scalability, paving the way for a more robust and efficient smart contract ecosystem in smart city applications.
This paper presents a blockchain-based health information self-management platform that leverages smart contracts and Non-Fungible Tokens (NFTs) technologies to ensure data integrity and secure access control. Existing health information systems face challenges related to data security, patient privacy, and interoperability. Our proposed platform addresses these issues through a decentralized architecture that utilizes NFTs for ownership management, InterPlanetary File System (IPFS) for distributed data storage, and smart contracts for access control. Experimental results verified that the system operated as intended, with complete tampering detection and accurate access control during ownership transfers, in line with the security guarantees inherent in blockchain technology. Although the distributed architecture results in longer response times (7836.4 ms), this is a trade-off to achieve cryptographically guaranteed security. Overall, the proposed system enhances data transparency and trust in the management of health information.
Daniel Commey, Benjamin Appiah, Griffith Selorm Klogo, Garth V. Crosby
Federated Learning (FL) enables collaborative model training on decentralized data without exposing raw data. However, the evaluation phase in FL may leak sensitive information through shared performance metrics. In this paper, we propose a novel protocol that incorporates Zero-Knowledge Proofs (ZKPs) to enable privacy-preserving and verifiable evaluation for FL. Instead of revealing raw loss values, clients generate a succinct proof asserting that their local loss is below a predefined threshold. Our approach is implemented without reliance on external APIs, using self-contained modules for federated learning simulation, ZKP circuit design, and experimental evaluation on both the MNIST and Human Activity Recognition (HAR) datasets. We focus on a threshold-based proof for a simple Convolutional Neural Network (CNN) model (for MNIST) and a multi-layer perceptron (MLP) model (for HAR), and evaluate the approach in terms of computational overhead, communication cost, and verifiability.
The globalization of digital infrastructures necessitates secure cross-border data transfers, yet existing governance frameworks struggle to reconcile regulatory transparency requirements with enterprise needs for confidentiality. Traditional approaches based on trusted execution environments or blockchain technologies face critical limitations, including prohibitive operational costs and technical inflexibility across cryptographic standards. This paper introduces a novel cryptographic framework that systematically addresses these challenges through three core innovations. First, we establish a lifecycle model integrating transmission, attestation, and verification phases with deterministic cryptographic constraints, ensuring continuous integrity monitoring across distributed systems. Second, our architecture implements non-intrusive compliance validation through zero-knowledge proofs and privacy-preserving verification protocols, eliminating raw data exposure while meeting diverse regulatory mandates. Third, the framework achieves interoperability across conflicting digital certification standards through adaptive policy mappings. Experimental evaluations demonstrate the solution's superiority over conventional approaches, showing significant improvements in verification efficiency, reduced resource consumption, and robust defense against tampering attacks. The proposed model supports multi-jurisdictional legal requirements through auditable cryptographic proofs and timestamped evidence chains, offering enterprises a practical pathway for compliant cross-border operations. By embedding regulatory logic into technical workflows, our approach advances secure global data ecosystems that balance sovereignty preservation with digital economy demands.
Sofia Sakka, Nikolaos Pavlidis, Vasiliki Liagkou, Ioannis Panges · 7 authors
The growing influence of technology in the healthcare industry has led to the creation of innovative applications that improve convenience, accessibility, and diagnostic accuracy. However, health applications face significant challenges concerning user privacy and data security, as they handle extremely sensitive personal and medical information. Privacy-Enhancing Technologies (PETs), such as Privacy-Attribute-based Credentials, Differential Privacy, and Federated Learning, have emerged as crucial tools to tackle these challenges. Despite their potential, PETs are not widely utilized due to technical and implementation obstacles. This research introduces a comprehensive framework for protecting health applications from privacy and security threats, with a specific emphasis on gamified mental health apps designed to manage Attention Deficit Hyperactivity Disorder (ADHD) in children. Acknowledging the heightened sensitivity of mental health data, especially in applications for children, our framework prioritizes user-centered design and strong privacy measures. We suggest an identity management system based on blockchain technology to ensure secure and transparent credential management and incorporate Federated Learning to enable privacy-preserving AI-driven predictions. These advancements ensure compliance with data protection regulations, like GDPR, while meeting the needs of various stakeholders, including children, parents, educators, and healthcare professionals.
Gulshan Kumar, Rahul Saha, Mauro Conti, Tai-hoon Kim
De-anonymization attacks in blockchains are significant concerns as they compromise the privacy of users on a public ledger. Such attacks, in the form of network analysis and transaction patterns, aim to link a blockchain address to the identity of its owner, potentially revealing sensitive information. Though researchers introduce various solutions using Tor, VPN, and i2P to protect against de-anonymization in blockchains, they have certain limitations: i) non-verification of the private transactions, ii) reveal of the transaction graph, and iii) requirement of a trusted setup that is itself vulnerable to the adversary. All these lead to the revocation of de-anonymization problems. In this paper, we show a novel privacy assurance framework for blockchains. The proposed framework is called De-Anonymization Withstanding Solution (DAWS). DAWS is the first privacy-preserved blockchain framework against de-anonymization attacks. DAWS uses privacy-classifying smart contract execution and a novel consensus called Proof-of-Privacy (PoPri). A set of experiments is executed on PoPri as well as DAWS. The blockchain transactions are modified by including user-defined privacy labels. DAWS can handle attacker advantage ≥0.008 with a privacy breach probability < 0.01% under our threat model. Besides, an improvement in the throughput of DAWS is noticed as compared to Ethereum (almost 80 times) with the Hyperledger configuration for consensus. The gas consumption improvement is 20%. All the listed features enhance the appeal of the proposed DAWS as a robust privacy-preserving solution against blockchain de-anonymization attacks.
Jul 11, 2025·The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences
Abstract. Environmental surveillance, emergency response, and smart city planning all require the use of geospatial data, which includes satellite imagery, cartographic records, and real-time GPS coordinates. The high sensitivity and value of location-specific information make it unsafe to store and transmit it through conventional, centralized means, which can result in privacy breaches, unauthorized manipulations, and potential misuse. This paper aims to design and implement a secure, blockchain-based framework that blends AES (Advanced Encryption Standard) and RSA (Rivest–Shamir–Adleman) key management, which addresses these challenges. The aim is to guarantee strong data confidentiality by using symmetric encryption, and to use public-key cryptography for granular access control and secure key distribution. The proposed system uses Ethereum smart contracts to connect encrypted data references to a decentralized ledger, ensuring tamper resistance and auditability. In the proposed system, a Python-based FastAPI backend is responsible for data ingestion, cleaning, encryption, and blockchain interaction, while a React frontend can upload datasets, generate encryption keys, and retrieve access permissions. Modular microservices and well-defined APIs can seamlessly integrate various components, such as data processing scripts and on-chain contract logic, during development. The system's scalability is demonstrated by evaluating its performance against various dataset sizes, which involves metrics such as encryption overhead, blockchain transaction costs, and smart contract execution times. The practical usability of the system in actual scenarios is demonstrated through user acceptance testing, which is crucial for adoption in resource-limited environments. The results show the proposed crypto-enhanced blockchain framework can significantly enhance geospatial data security while still maintaining operational efficiency. Integration with zero-knowledge proofs may be explored in future work to enhance privacy, mitigate energy costs through alternative consensus algorithms, and enhance resilience in multi-network ecosystems through cross-chain interoperability.
Carlos Beis-Penedo, Rebeca P. Díaz-Redondo, Ana Fernandez-Vilas, Manuel Fernández‐Veiga · 5 authors
Collaborative machine learning in sensitive domains demands scalable, privacy-aware and access-controlled solutions for enterprise-grade deployment. Conventional federated learning (FL) relies on a central server, introducing single points of failure and privacy risks, while split learning (SL) partitions models for privacy but scales poorly because of sequential training. We present HLF-FSL, a decentralized architecture that combines federated split learning (FSL) with the permissioned blockchain Hyperledger Fabric (HLF). Chaincode orchestrates split-model execution and peer-to-peer aggregation without a central coordinator, leveraging HLF’s transient fields and Private Data Collections (PDCs) to keep raw data and model activations off-chain and access-controlled. On CIFAR-10, MNIST and ImageNet-Mini, HLF-FSL matches the accuracy of a standard server-coordinated FSL baseline while reducing per-epoch training time versus Ethereum-based baselines. Performance and scalability tests quantify the Fabric coordination overhead via a component-level breakdown of SDK-facing latencies and communication volumes; empirically, this overhead increases wall-clock epoch time while preserving the same accuracy-vs-epoch behavior as a FedSplit Learning baseline.