With the development of the era of big data, the demand for data sharing and usage is increasing, especially in the era of the internet of things, thus putting forward a keen demand for data exchanging and data trading. However, the existing data exchanging and trading platforms are usually centralised and users have to trust platforms. This paper proposes a secure and fair exchanging and trading protocol based on blockchain and smart contracts, especially, self-governance without relying on centralised trust. By using the protocol, it can guarantee fairness to defend against trade cheating, and security for data confidentiality. It can also guarantee efficiency by transferring data links instead of data between data owners and data buyers. The extensive analysis justified that the proposed scheme can facilitate self-exchanging and self-trading for big data in a secure, fair and efficient manner.
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive, in which a prover convinces a verifier that a given statement is true without leaking any additional information.However, existing zkSNARKs suffer from high computation overhead in the proof generation.This limits the applications of zkSNARKs, such as private payments, private smart contracts, and anonymous credentials.Private delegation has become a prominent way to accelerate proof generation.In this work, we propose Siniel, an efficient private delegation framework for zkSNARKs constructed from polynomial interactive oracle proof (PIOP) and polynomial commitment scheme (PCS).Our protocol allows a computationally limited prover (a.k.a.delegator) to delegate its expensive prover computation to several workers without leaking any information about the private witness.Most importantly, compared with the recent work EOS (USENIX'23), the state-of-the-art zkSNARK prover delegation framework, a prover in Siniel needs not to engage in the MPC protocol after sending its shares of private witness.This means that a Siniel prover can outsource the entire computation to the workers.We compare Siniel with EOS and show significant performance advantages of the former.The experimental results show that, under low bandwidth conditions (10MBps), Siniel saves about 16% time for delegators than that of EOS, whereas under high bandwidth conditions (1000MBps), Siniel saves about 80% than EOS.
Natalia Borgoñós García, María Hernández Padilla, Antonio Fernando Skarmeta Gómez
Abstract Data Spaces are ecosystems designed to allow multiple organizations or companies to share data in a secure manner. Despite the potential of these technologies, they encounter a number of challenges and privacy issues that limit their use. Privacy Preserving Enablers are mechanisms developed to tackle these difficulties, ensuring data integrity and access control. This paper aims to analyze the role of some Privacy Preserving Enablers and its integration with Connectors in the context of Data Spaces. The research will focus on key enablers, including a Self-Sovereign Identity with Zero-Knowledge Proof, which is a privacy preserving approach that allows users to verify their identity and attributes without the need to disclose underlying data, ensuring their privacy. Additionally, the usage of Sticky Policies instantiated through Attribute-Based Encryption attaches control policies into the encrypted data in order to have an attribute-based access control, enhancing its security. The application of Policies Enforcement assure the consistent application of policies and the maintenance of the security within the Data Space.
Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FL—such as high communication overhead, resource limitations, and convergence inefficiencies—the method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average +8.4% improvement in accuracy, -16.2% lower latency, -35.1% reduction in memory usage, and -2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.
Neural network inference in cloud service offers tangible benefits to users, from individuals and small institutions to large companies. However, two crucial concerns must be addressed. The first arises in satisfying the privacy of the model, the input data, and the inference results throughout the inference process. The second pertains to verifying that the inferences are derived from the designated neural network model. Although Secure Multi-Party Computation (MPC) and Zero-Knowledge Proof (ZKP) are typically adopted to mitigate such issues, the major challenge lies in achieving privacy preservation and verifiability simultaneously. In this study, we address both issues by proposing VSecNN, a verifiable and privacy-preserving neural network inference scheme. Specifically, we integrate MPC with the Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK) protocol to achieve zero-knowledge proof generation for multiple parties. Subsequently, we perform adaptive optimizations on the multi-party proof generation approach to align with the neural network, thereby achieving both privacy-preserving capabilities and verifiability. Experimental results demonstrate an improvement in the efficiency. For example, the computation time for completing our multi-party proof generation could be as low as 1.7 times that of the single-party proof generation, while the verification requires only 169ms on the MNIST dataset.
The rapid advancement of technology and the widespread adoption of various IoT devices have resulted in significant challenges in handling and analyzing the vast and diverse volumes of data they produce. Conventional machine learning methodologies, which require data aggregation in a centralized repository for analysis, are often unfeasible due to the significant data volume, limited communication bandwidth, and strict security and privacy requirements. Federated Learning (FL) presents an innovative resolution by facilitating data analysis to take place directly at the data origin, federating the outcomes to generate results comparable to centralized processing. Due to the advancements in growth of federated learning architecture, a number of open-source frameworks have been established to implement this strategy. These frameworks facilitate a range of applications, including healthcare and finance, as well as IoT and smart city initiatives, by enabling data to remain decentralized while still supporting the development of comprehensive analytical models. This study provides a comprehensive evaluation of these established open-source FL frameworks, examining their applicability across various sectors and highlighting their advantages and limitations. The authors examine the various architectural structures, supported algorithms, level of implementation ease, and community support of each framework. The assessment additionally takes into account the scalability of these frameworks and their flexibility in various data scenarios. Through the assessment of the current capabilities and developmental phases of these FL frameworks, this study aims to support practitioners and researchers in identifying the most suitable tools for their specific needs. This analysis highlights the significance of FL in facilitating secure and effective data analysis in the contemporary technological landscape.
In recent years, the refinements in industrial processes and the increasing complexity of managing privacy-sensitive data from Industrial Internet of Things (IIoT) devices, have highlighted the critical need for secure, robust, and adaptive data management solutions. In this work, we propose a smart contract-assisted access control scheme for IIoT, which employs the Attribute-Based Access Control (ABAC) model to set access permissions for different industrial components. We defined a storage model and data format for private data through the design and deployment of smart contracts to manage system operations and access policies. In addition, the bloom filter component is deployed to optimize the efficiency of contract management and system performance. Experimental results show that in the real-world simulations, Hyper-IIoT shows well-controlled contract execution time, stable system throughput and fast consensus process, and is capable of handling high throughput and effective consensus in distributed systems even in large-scale request scenarios.
随着工业物联网的快速发展，在分布式环境下高效训练机器学习模型，同时保障数据隐私与系统安全，已成为亟待解决的关键问题。传统联邦学习虽然在一定程度上缓解了数据泄露风险，但仍面临中心化服务器易受攻击、投毒威胁以及模型更新透明度不足等挑战。为此，文章提出了一种基于区块链的去中心化联邦学习框架(BCS-FL)。该框架结合区块链技术与权益证明(PoS)共识机制，实现联邦学习的去中心化训练、筛选及存储，从而提升系统的安全性、透明度和可靠性。具体而言，在本地设备层，各智能工厂(客户端)基于自身数据训练局部模型，并经过裁剪和差分隐私保护后，生成局部模型包上传至验证层；在去中心化验证层，由PoS选出的验证节点计算局部模型间的余弦相似度以及损失函数差值，以筛选可信、高质量的客户端模型；在区块链存储层，矿工节点验证候选全局模型的哈希值、时间戳以及客户端签名，以确保模型版本的一致性，并通过PBFT共识将其存储至区块链，供所有客户端下载与同步，确保模型更新的可追溯性和抗篡改能力。With the rapid development of the Industrial Internet of Things, efficiently training machine learning models in distributed environments while ensuring data privacy and system security has become a critical challenge. Although traditional federated learning mitigates the risk of data leakage to some extent, it still faces challenges such as the vulnerability of centralized servers to attacks, poisoning threats, and insufficient transparency in model updates. To address these issues, this paper proposes a blockchain-based, decentralized, federated learning framework (BCS-FL). By integrating blockchain technology with the Proof-of-Stake (PoS) consensus mechanism, BCS-FL enables decentralized training, selection, and storage of federated learning models, thereby enhancing system security, transparency, and reliability. Specifically, at the local device layer, intelligent factories (clients) train local models based on their own data and generate local model packages after pruning and differential privacy protection before uploading them to the coordination layer. At the decentralized coordination layer, coordination nodes selected through PoS compute the cosine similarity and loss function differences between local models to filter out reliable and high-quality client models. At the blockchain storage layer, miner nodes verify the hash values, timestamps, and client signatures of candidate global models to ensure model version consistency. The final global model is stored on the blockchain through PBFT consensus, allowing all clients to download and synchronize it, ensuring model update traceability and tamper resistance.
With the increasing use of online services, the protection of the privacy of users becomes more and more important. This is particularly critical as authentication and authorization as realized on the Internet nowadays, typically relies on centralized identity management solutions. Although those are very convenient from a user's perspective, they are quite intrusive from a privacy perspective and are currently far from implementing the concept of data minimization. Fortunately, cryptography offers exciting primitives such as zero-knowledge proofs and advanced signature schemes to realize various forms of so-called anonymous credentials. Such primitives allow to realize online authentication and authorization with a high level of built-in privacy protection (what we call privacy-preserving authentication). Though these primitives have already been researched for various decades and are well understood in the research community, unfortunately, they lack widespread adoption. In this paper, we look at the problems, what cryptography can do, some deployment examples, and barriers to widespread adoption. Latter using the example of the EU Digital Identity Wallet (EUDIW) and the recent discussion and feedback from cryptography experts around this topic. We also briefly comment on the transition to post-quantum cryptography.
This study introduces a secure and scalable hybrid trust management system for Vehicular Ad Hoc Networks (VANETs), designed to detect and mitigate diverse malicious activities, including Sybil attacks, Denial of Service (DoS), and data forgery. Integrating entropy-based anomaly detection, Bayesian reputation updating, and the IOTA Tangle as a distributed ledger, the system ensures robust, real-time trust evaluation in dynamic vehicular environments. Rigorous testing against adversarial scenarios demonstrated high detection accuracy, low latency, and resilience under varied attack conditions. Key contributions include enhanced scalability through edge computing, secure reputation storage using Distributed Ledger Technology (DLT), and real-time anomaly detection.
Lingyan Xue, Haiping Huang, Fu Xiao, Qi Li · 5 authors
Blockchain transaction privacy is a highly researched topic across various application scenarios. Current privacy-preserving schemes in blockchain employ advanced cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, to balance transaction privacy with regulatory requirements. However, these schemes encounter challenges, including computational inefficiency, data expansion, and overlooked metadata privacy, such as timestamp protection. In this paper, we first propose a privacy-enhanced traceable anonymous transaction scheme based on data transaction scenarios. This scheme integrates ring signature and Merkle hash tree techniques, effectively shortening the signature size and optimizing the verification process compared to existing combinations of ring signatures and zero-knowledge proofs. A novel verifiable timestamp privacy protection method is introduced, which obfuscates timestamps to prevent tampering without compromising integrity. To enhance scalability, this method extends to multiple transaction processing scenarios and implements a timestamp-sharing strategy to reduce the computational burden. It also allows tracking authorities to monitor the long-term addresses of both transaction parties if necessary. Rigorous security analysis and extensive experimental evaluations demonstrate that this scheme achieves superior privacy, traceability, and scalability compared to existing approaches.
Federated Learning (FL) transformed decentralized machine learning by allowing joint model training without mutually sharing raw data, hence being especially useful in privacy-sensitive applications like healthcare, e-commerce, and finance. Even with its privacy-focused architecture, FL is vulnerable to a range of security attacks such as data poisoning, model inversion, membership inference attacks, and communication interception. These attacks compromise the confidentiality of patients in healthcare, consumer data privacy in e-commerce, and financial safety in banking, thus necessitating effective privacy-preserving mechanisms. This survey presents a classification of security threats in FL, grouping them by their source, effect, and attack mode. We review state-of-the-art countermeasures, such as differential privacy, secure multi-party computation, homomorphic encryption, and resilient aggregation methods, their effectiveness, trade-offs, and real-world applicability to FL. In medicine, FL enables joint disease diagnosis without compromising patient confidentiality; in online shopping, it provides personalized suggestions without revealing customer tastes; and in banking, it improves fraud detection without violating regulatory requirements. In addition, we discuss future horizons in privacy-preserving FL, including adversarial robustness, blockchain-protected models, and tailored FL architectures, improving security and resiliency in these domains. We also discuss the balancing problems between security, accuracy, and computational efficiency with possible trade-offs in scaling privacy-preserving FL By analyzing threats and mitigation strategies systematically, this paper will provide direction to future research on designing secure, scalable, and privacy-preserving FL frameworks for the changing healthcare, e-commerce, and finance needs.
Open access
Privacy-Preserving Technologies in Data
Cryptography and Data Security
Artificial Intelligence in Healthcare and Education