Mohammed Alghazwi, Dewi Davies-Batista, Dimka Karastoyanova, Fatih Türkmen
Aggregate statistics play an important role in extracting meaningful insights from distributed data while preserving privacy. A growing number of application domains, such as healthcare, utilize these statistics in advancing research and improving patient care. In this work, we explore the challenge of input validation and public verifiability within privacy-preserving aggregation protocols. We address the scenario in which a party receives data from multiple sources and must verify the validity of the input and correctness of the computations over this data to third parties, such as auditors, while ensuring input data privacy. To achieve this, we propose the "VPAS" protocol, which satisfies these requirements. Our protocol utilizes homomorphic encryption for data privacy, and employs Zero-Knowledge Proofs (ZKP) and a blockchain system for input validation and public verifiability. We constructed VPAS by extending existing verifiable encryption schemes into secure protocols that enable N clients to encrypt, aggregate, and subsequently release the final result to a collector in a verifiable manner. We implemented and experimentally evaluated VPAS with regard to encryption costs, proof generation, and verification. The findings indicate that the overhead associated with verifiability in our protocol is 10x lower than that incurred by simply using conventional zkSNARKs. This enhanced efficiency makes it feasible to apply input validation with public verifiability across a wider range of applications or use cases that can tolerate moderate computational overhead associated with proof generation.
The explosive growth of Large-Language Models (LLMs), particularly Generative Pre-trained Transformer (GPT) models, has revolutionised fields ranging from natural language processing to creative writing. Yet, their reliance on vast, often unverified data sources introduces a critical vulnerability: unreliability and security concerns. Traditional GPT models, while impressive in their capabilities, struggle with limited factual accuracy and susceptibility to manipulation by biased or malicious data. This poses a significant risk in professional and personal environments where sensitive or mission-critical data is paramount. This work tackles this challenge head-on by proposing a novel approach to enhance GPT security and reliability: leveraging Zero-Knowledge Proofs (ZKPs). Unlike traditional cryptographic methods that require sensitive data exchange, ZKPs allow one party to convincingly prove the truth of a statement, without revealing the underlying information. In the context of GPTs, ZKPs can validate the legitimacy and quality of data sources used in GPT computations, combating data manipulation and misinformation. This ensures trustworthy outputs, even when incorporating third-party data (TPD). ZKPs can securely verify user identities and access privileges, preventing unauthorised access to sensitive data and functionality. This protects critical information and promotes responsible LLM usage. ZKPs can identify and filter out manipulative prompts designed to elicit harmful or biased responses from GPTs. This safeguards against malicious actors and promotes ethical LLM development. ZKPs facilitate training specialised GPT models on targeted datasets, resulting in deeper understanding and more accurate outputs within specific domains. This allows the creation of ‘expert-GPT’ applications in specialised fields like healthcare, finance, and legal services. The integration of ZKPs into GPT models represents a crucial step towards overcoming trust and security barriers. Our research demonstrates the viability and efficacy of this approach, with our ZKP-based authentication system achieving promising results in data verification, user control, and malicious prompt detection. These findings lay the groundwork for a future where GPTs, empowered by ZKPs, operate with unwavering integrity, fostering trust and accelerating ethical AI development across diverse domains.
Raghavendra Varma Cherukuri, G. Lavanya Devi, Neelapu Ramesh
Federated learning is a privacy-preserving machine learning technique that allows mutually distrusting parties to collaboratively train a model without sharing their data. Most federated learning techniques require a centralized aggregator that stores and aggregates models received from multiple parties. However, having a centralized entity may lead to a single point of failure problem. Another problem of federated learning is the leakage of sensitive data through model updates. To address these issues, we propose a Blockchain-based protocol for federated learning. Our protocol uses Blockchain as a model aggregator solving single-point-of-failure problems. Also, we use a Blockchain-based privacy-preserving technique to avoid data leakage problems. We also incorporate a Blockchain-based incentive distribution module to distribute incentives to model contributors. We perform experiments with well-known datasets and show that the proposed model's accuracy is close to that of a centralized aggregator. We also show the overhead of Blockchain by implementing the protocol and running it on Ethereum Blockchain.
Blockchain evidence is a technical method for evidence storage, transmission and fixation. Its evidential value has a dual nature, reflected in the fact that it cannot be absolutely tamper-resistant, can only provide periodic assurance of evidence authenticity and the commonly used consortium chains do not possess all the benefits of public chains. Simultaneously, blockchain evidence occupies a unique status within the entire evidence system, it serves as an evidentiary storage mechanism, is essentially an electronic evidence reflecting both the evidence collection process and outcome and its notarisation and forensic examination documents are a type of opinion evidence. It is evident that blockchain evidence does not emerge in a vacuum, rather than serving as a mere replacement for traditional evidence, blockchain evidence represents an upgrade in the functionality and effectiveness of traditional evidence. From this perspective, the improvement of blockchain evidence rules should align with the basic position of ‘technological neutrality’, which means that although technological evolution can lead to rapid changes, legislators are not always required to cater to these dynamic demands. It is essential to distinguish between on-chain and off-chain when addressing issues of authenticity, hearsay and originality, and improvements proposing should within the frameworks of existing electronic evidence rules and opinion evidence rules, thereby unlocking the potential of blockchain evidence.
Ruizhe Yang, Tonghui Zhao, F. Richard Yu, Meng Li · 6 authors
Federated learning, leveraging distributed data from multiple nodes to train a common model, allows for the use of more data to improve the model while also protecting the privacy of original data. However, challenges still exist in ensuring privacy and security within the interactions. To address these issues, this paper proposes a federated learning approach that incorporates blockchain, homomorphic encryption, and reputation. Using homomorphic encryption, edge nodes possessing local data can complete the training of ciphertext models, with their contributions to the aggregation being evaluated by a reputation mechanism. Both models and reputations are documented and verified on the blockchain through consensus process, which then determines the rewards based on the incentive mechanism. This approach not only incentivizes participation in training, but also ensures the privacy of data and models through encryption. Additionally, it addresses security risks associated with both data and network attacks, ultimately leading to a highly accurate trained model. To enhance the efficiency of learning and the performance of the model, a joint adaptive aggregation and resource optimization algorithm is introduced. Finally, simulations and analyses demonstrate that the proposed scheme enhances learning accuracy while maintaining privacy and security.
Purpose The purpose of this paper is to study the following two issues regarding blockchain crowdsourcing. First, to design smart contracts with lower consumption to meet the needs of blockchain crowdsourcing services and also need to design better interaction modes to further reduce the cost of blockchain crowdsourcing services. Second, to design an effective privacy protection mechanism to protect user privacy while still providing high-quality crowdsourcing services for location-sensitive multiskilled mobile space crowdsourcing scenarios and blockchain exposure issues. Design/methodology/approach This paper proposes a blockchain-based privacy-preserving crowdsourcing model for multiskill mobile spaces. The model in this paper uses the zero-knowledge proof method to make the requester believe that the user is within a certain location without the user providing specific location information, thereby protecting the user’s location information and other privacy. In addition, through off-chain calculation and on-chain verification methods, gas consumption is also optimized. Findings This study deployed the model on Ethereum for testing. This study found that the privacy protection is feasible and the gas optimization is obvious. Originality/value This study designed a mobile space crowdsourcing based on a zero-knowledge proof privacy protection mechanism and optimized gas consumption.
Federated learning (FL) enables collaborative training of machine learning models across distributed medical data sources without compromising privacy. However, applying FL to medical image analysis presents challenges like high communication overhead and data heterogeneity. This paper proposes novel FL techniques using explainable artificial intelligence (XAI) for efficient, accurate, and trustworthy analysis. A heterogeneity-aware causal learning approach selectively sparsifies model weights based on their causal contributions, significantly reducing communication requirements while retaining performance and improving interpretability. Furthermore, blockchain provides decentralized quality assessment of client datasets. The assessment scores adjust aggregation weights so higher-quality data has more influence during training, improving model generalization. Comprehensive experiments show our XAI-integrated FL framework enhances efficiency, accuracy and interpretability. The causal learning method decreases communication overhead while maintaining segmentation accuracy. The blockchain-based data valuation mitigates issues from low-quality local datasets. Our framework provides essential model explanations and trust mechanisms, making FL viable for clinical adoption in medical image analysis.
Privacy-Preserving Technologies in Data
Artificial Intelligence in Healthcare and Education
We construct perfect zero-knowledge probabilistically checkable proofs (PZK-PCPs) for every language in #P. This is the first construction of a PZK-PCP for any language outside BPP. Furthermore, unlike previous constructions of (statistical) zero-knowledge PCPs, our construction simultaneously achieves non-adaptivity and zero knowledge against arbitrary (adaptive) polynomial-time malicious verifiers. Our construction consists of a novel masked sumcheck PCP, which uses the combinatorial nullstellen- satz to obtain antisymmetric structure within the hypercube and randomness outside of it. To prove zero knowledge, we introduce the notion of locally simulatable encodings: randomised encodings in which every local view of the encoding can be efficiently sampled given a local view of the message. We show that the code arising from the sumcheck protocol (the Reed–Muller code augmented with subcube sums) admits a locally simulatable encoding. This reduces the algebraic problem of simulating our masked sumcheck to a combinatorial property of antisymmetric functions.
The wide application of smart contracts allows industry companies to implement some complex distributed collaborative businesses, which involve the calculation of complex functions, such as matrix operations. However, complex functions such as matrix operations are difficult to implement on Ethereum Virtual Machine (EVM)-based smart contract platforms due to their distributed security environment limitations. Existing off-chain methods often result in a significant reduction in contract execution efficiency, thus a platform software development kit interface implementation method has become a feasible way to reduce overheads, but this method cannot verify operation correctness and may leak sensitive user data. To solve the above problems, we propose a verifiable EVM-based smart contract cross-language implementation scheme for complex operations, especially matrix operations, which can guarantee operation correctness and user privacy while ensuring computational efficiency. In this scheme, a verifiable interaction process is designed to verify the computation process and results, and a matrix blinding technology is introduced to protect sensitive user data in the calculation process. The security analysis and performance tests show that the proposed scheme can satisfy the correctness and privacy of the cross-language implementation of smart contracts at a small additional efficiency cost.
In contemporary data-driven economies, data has become a valuable digital asset that is eligible for trading and monetization. Peer-to-peer (P2P) marketplaces play a crucial role in establishing direct connections between data providers and consumers. However, traditional data marketplaces exhibit inadequacies. Functioning as centralized platforms, they suffer from issues such as insufficient trust, transparency, fairness, accountability, and security. Moreover, users lack consent and ownership control over their data. To address these issues, we propose DataMesh+, an innovative blockchain-powered, decentralized P2P data exchange model for self-sovereign data marketplaces. This user-centric decentralized approach leverages blockchain-based smart contracts to enable fair, transparent, reliable, and secure data trading marketplaces, empowering users to retain full sovereignty and control over their data. In this article, we describe the design and implementation of our approach, which was developed to demonstrate its feasibility. We evaluated the model’s acceptability and reliability through experimental testing and validation. Furthermore, we assessed the security and performance in terms of smart contract deployment and transaction execution costs, as well as the blockchain and storage network performance.
Federated learning combines with fog computing to transform data sharing into model sharing, which solves the issues of data isolation and privacy disclosure in fog computing. However, existing studies focus on centralized single-layer aggregation federated learning architecture, which lack the consideration of cross-domain and asynchronous robustness of federated learning, and rarely integrate verification mechanisms from the perspective of incentives. To address the above challenges, we propose a Blockchain and Signcryption enabled Asynchronous Federated Learning (BSAFL) framework based on dual aggregation for asynchronous cross-domain federated learning scenarios. In particular, we first design two types of signcryption schemes to secure the interaction and access control of collaborative learning between domains. Second, we construct a differential privacy approach that adaptively adjusts privacy budgets to ensure data privacy and local models' availability of intra-domain user. Furthermore, we propose an asynchronous aggregation solution that incorporates consensus verification and elastic participation using blockchain. Finally, security analysis demonstrates the security and privacy effectiveness of BSAFL, and the evaluation on real datasets further validates the high model accuracy and performance of BSAFL.
The utilization of blockchain technology, with its decentralized and immutable ledger, has revolutionized the way data is stored and distributed. However, concerns about data security persist within blockchain networks. To address these issues, this project integrates various technologies including smart contracts, AES encryption, searchable encryption, and Byzantine Fault Tolerant (BFT) algorithms. AES encryption and BFT algorithms are employed to enhance data privacy and integrity, while smart contracts regulate access and searchable encryption facilitates the retrieval of private data. By implementing these technologies, the project aims to enhance data security, enable private data retrieval, and ensure reliable data querying within blockchain systems. Smart contracts are utilized to restrict access, allowing only approved users to interact with sensitive data, thus limiting exposure to authorized parties. Searchable encryption enables anonymous querying while preserving privacy by concealing basic information during searches. The use of AES encryption secures data, preventing unauthorized alterations, while BFT algorithms enhance system dependability by safeguarding data integrity and consistency against malicious nodes. Through rigorous simulations and testing, the project's approach is evaluated, demonstrating its effectiveness in regulating access, retrieving private data, protecting data integrity, and ensuring system reliability. Overall, this approach addresses data privacy and security concerns inherent in blockchain technology, preventing data breaches and maintaining the integrity of blockchain records even under adversarial conditions.
The proliferation of Vehicular Ad-hoc Networks (VANETs) highlights the crucial need for secure, privacy-protecting inter-vehicle communication. By integrating blockchain technology with a safe certificate-less aggregate signature process, this research presents a novel approach to enhancing VANET security. Traditional VANET signature systems that rely on PKI and certificates have their limitations solved by the suggested approach. Using a certificate-less paradigm eliminates the requirement for a certificate authority when creating keys, making key exposure and compromise less probable. By combining several signatures into one smaller one, the aggregate signature method significantly decreases network communication cost. The paper introduces blockchain technology as an extra component to the certificate-less aggregate signature, further ensuring the honesty and openness of the VANET. The blockchain records and validates transactions, aggregate signatures, key alterations, and more using its distributed and immutable ledger. This not only improves the signature scheme but also creates a VANET environment that is trustworthy and robust.
G. Senthilkumar, K. N. Madhusudhan, Y. Jeyasheela, P. Ajitha
Large amounts of processing resources are required for the sensed raw big data processing during the data generation process.Furthermore, as sensed data are typically privacy sensitive, blockchain technology can be used to ensure the privacy concerns.This study examines a multiuser mobile offloading network that consists of a cloud server located remotely and an edge node.We formulate the offloading problem as the joint optimization of task offloading decision making of all users, the computation resource allocation among the edge executing applications, and the radio resource assignment among all the remote-processing applications.The goal is to minimize the maximum weighted cost of all users.When compared to other benchmark approaches, the simulation results show that the proposed algorithm achieves optimal results in terms of both energy consumption and delay as a result of collaboration.Finally the resource allocation and optimal offloading strategy with 93% efficiency is obtained.
The advent of the digital era and computer-based remote communications has significantly enhanced the applicability of various sciences over the past two decades, notably data science (DS) and cryptography (CG). Data science involves clustering and categorizing unstructured data, while cryptography ensures security and privacy aspects. Despite certain CG laws and requirements mandating fully randomized or pseudonoise outputs from CG primitives and schemes, it appears that CG policies might impede data scientists from working on ciphers or analyzing information systems supporting security and privacy services. However, this study posits that CG does not entirely preclude data scientists from operating in the presence of ciphers, as there are several examples of successful collaborations, including homomorphic encryption schemes, searchable encryption algorithms, secret-sharing protocols, and protocols offering conditional privacy. These instances, along with others, indicate numerous potential solutions for fostering collaboration between DS and CG. Therefore, this study classifies the challenges faced by DS and CG into three distinct groups: challenging problems (which can be conditionally solved and are currently available to use; e.g., using secret sharing protocols, zero-knowledge proofs, partial homomorphic encryption algorithms, etc.), open problems (where proofs to solve exist but remain unsolved and is now considered as open problems; e.g., proposing efficient functional encryption algorithm, fully homomorphic encryption scheme, etc.), and hard problems (infeasible to solve with current knowledge and tools). Ultimately, the paper will address specific solutions and outline future directions to tackle the challenges arising at the intersection of DS and CG, such as providing specific access for DS experts in secret-sharing algorithms, assigning data index dimensions to DS experts in ultra-dimension encryption algorithms, defining some functional keys in functional encryption schemes for DS experts, and giving limited shares of data to them for analytics.
Blockchain technology is a decentralized and secure paradigm for data processing, sharing, and storing. It relies on consensus protocol for all decisions, which focuses on computational and resource capability. For example, proof of work (PoW) and proof of stake (PoS) are the most famous consensus protocols that are currently used. However, these current consensus protocols are required to recruit a node with a high computational or a large amount of cryptocurrency to act as a miner node and to generate a new block. Unfortunately, these PoW and PoS protocols could be impractical for adoption in today’s technological fields, such as the Internet of Things and healthcare. In addition, these protocols are susceptible to flexibility, security, and fairness issues, as they are discussed in detail in this work. Therefore, this paper introduces a proof of fairness (PoF) as a dynamic and secure consensus protocol for enhancing the mining selection process. The selection of the miner node is influenced by numerous factors, including the time required to generate a block based on the transaction’s sensitivity. Firstly, a reverse auction mechanism is designed as an incentive mechanism to encourage all nodes to participate in the miner selection process. In a reverse auction, each node will draw its strategy based on its computational capability and claimed cost. Secondly, an expressive language is developed to categorize transaction types based on their sensitivity to processing time, ensuring compatibility with our miner selection process. Thirdly, a homomorphic concept is designed as a security and privacy scheme to protect the bidder’s data confidentiality. Finally, an extensive evaluation involving numerical analysis was carried out to assess the efficiency of the suggested PoF protocol, which confirms that the proposed PoF is dynamic and more efficient than current PoW and PoS consensus protocols.
Jingxue Chen, Eric Wang, Gautam Srivastava, Turki Ali Alghamdi · 7 authors
The space-air-ground-sea three-dimensional (3D) network is a comprehensive communication network system. This 3D network combines the extensive coverage of satellite communications, the adaptability of unmanned aerial vehicle (UAV) communications, the reliability of terrestrial communications, and the necessity of maritime communications. These networks generate enormous amounts of data, and training machine learning (ML) models on this data will have a significant impact on the industry. At the same time, the availability of such data poses numerous security threats, which can be overcome by Federated Learning (FL). The decentralized training in FL can provide a universal model from local data generated by the 3D network However, most existing FL frameworks have a centralized server, which questions the credibility, single-point failure, and global confidence. To solve these problems, industrial blockchain technology has received much attention by replacing centralized servers in traditional FL, which offers a promising approach to address key issues such as data privacy and security. In a blockchain-based system, digital signature is the core component for ensuring data integrity and system security, however, private key disclosure can pose significant risks. The security can be enhanced by using threshold signature, which provides a more reliable foundation for FL by storing keys in multiple nodes and requiring multiple nodes to collaborate to generate signatures. In this paper, we propose a Threshold signing scheme for ISO/IEC Digital Signature Standards (TDSS) in industrial blockchain. The TDSS scheme helps FL to achieve truly distributed decentralization for unified space-air-ground-sea model training. The TDSS scheme exploits the SM-2 digital signature algorithm in the ISO/IEC standard when t out of n nodes in industrial blockchain interact with each other to calculate the signature. The experimental results and analyses show that the TDSS scheme has provable security and efficient against security attacks, which can be applied to large-scale threshold signing scenarios.
The advent of central bank digital currencies (CBDCs) has underscored multifaceted privacy concerns identified in literature, particularly in user monitoring and data security. PETs, such as zero-knowledge proofs and homomorphic encryption, emerge as critical in reconciling regulatory compliance with user anonymity. Encryption safeguards CBDC transactions, while multifactor authentication bolsters transaction integrity. Governance structures play a pivotal role in upholding stringent security standards. This discussion navigates through diverse CBDC models and their privacy implications, probing into the intricacies of user monitoring, data breaches, and biometric data protection. Future research should aim to refine PETs, harmonize regulatory frameworks, and fortify biometric data security. The pursuit of robust privacy measures necessitates a delicate equilibrium between technological innovation and regulatory efficacy, fostering trust and compliance amidst the evolving landscape of CBDC privacy concerns.
Supported by cloud computing,FederatedLearning (FL) has experienced rapid advancement, as a promising technique to motivate clients to collaboratively train models without sharing local data. To improve the security and fairness of FL implementation, numerousBlockchain-empoweredFederatedLearning (BFL) frameworks have emerged accordingly. Among them, consensus algorithms play a pivotal role in determining the scalability, security, and consistency of BFL systems. Existing consensus solutions to block producer selection and reward allocation either focus on well-resourced scenarios or accommodate BFL based on clients' contributions to model training. However, these approaches limit consensus efficiency and undermine reward fairness, due to involving intricate consensus processes, disregarding clients' contributions during blockchain consensus, and failing to address lazy client problems (malicious clients plagiarizing local model updates from others to reap rewards). Given the aforementioned challenges, we make the first attempt to design a joint solution for efficient consensus and fair reward allocation in heterogeneous BFL systems with lazy clients. Specifically, we introduce a generalizable BFL workflow that can address lazy client problems well. Based on it, the global contribution of BFL clients is decoupled into five dominant metrics, and the block producer selection problem is formulated as a reward-constraint contribution maximization problem. By addressing this problem, the optimal block producer that maximizes global contribution can be identified to orchestrate consensus processes, and rewards are distributed to clients in proportion to their respective global contributions. To achieve it, we develop aContext-awareProof-of-Contribution consensus algorithm named CPoC to reach consensus and incentive simultaneously, followed by theoretical analysis of lazy client problems and privacy issues. Empirical results on widely-used datasets demonstrate the effectiveness of our design in improving consensus efficiency and maximizing global contribution.
To solve the problem of climate warming, countries around the world have paid special attention to the construction of carbon governance. Carbon emission accounting is an important policy tool to control the vented CO2. But at present, there are third-party agencies in carbon emission accounting that cannot ensure the fairness and impartiality of accounting, and there may be risks such as illegal use and leakage of sensitive information in the process of carbon emission data transmission. Therefore, We design the blockchain-based carbon emission security accounting scheme (BCESAS) and propose cross-chain verification contract to ensure the efficiency of cross-chain information accounting. In addition, bilinear pairing is used to ensure data integrity, and we encrypt private data using an improved and more secure homomorphic encryption algorithm to ensure that privacy is not leaked during the transfer of carbon emission data, which is more efficent than other homomorphic encryption algorithms. We also use reputation mechanism to regulate the behavior of carbon emission auditors. The theoretical and experimental analysis demonstrates that BCESAS can verify the integrity, correctness and privacy of cross-chain data calculation result effectively, realizing secure and reliable expansion of blockchain.
Nowadays, Internet of Things platforms are being deployed in a wide range of application domains. Some of these include use cases with security requirements, where the data generated by an IoT node is the basis for making safety-critical or liability-critical decisions at system level. The challenge is to develop a solution for data exchange while proving and verifying the authenticity of the data from end-to-end. In line with this objective, this paper proposes a novel solution with the proper protocols to provide Trust in Data, making use of two Roots of Trust that are the IOTA Distributed Ledger Technology and the Trusted Platform Module. The paper presents the design of the proposed solution and discusses the key design aspects and relevant trade-offs. The paper concludes with a Proof-of-Concept implementation and an experimental evaluation to confirm its feasibility and to assess the achievable performance.
Federated learning (FL) is a distributed machine learning approach that protects user data privacy by training models locally on clients and aggregating them on a parameter server. While effective at preserving privacy, FL systems face limitations such as single points of failure, lack of incentives, and inadequate security. To address these challenges, blockchain technology is integrated into FL systems to provide stronger security, fairness, and scalability. However, blockchain-empowered FL (BC-FL) systems introduce additional demands on network, computing, and storage resources. This survey provides a comprehensive review of recent research on BC-FL systems, analyzing the benefits and challenges associated with blockchain integration. We explore why blockchain is applicable to FL, how it can be implemented, and the challenges and existing solutions for its integration. Additionally, we offer insights on future research directions for the BC-FL system.