Kavian Amirmozafarisabet, Meisam Namazi, Mohammad Naserameri
Rapid deployment of AI, particularly in sectors such as healthcare, finance and smart infrastructure, necessitates reliable capabilities (1) to assure data integrity (2) model update verifiability and (3) privacy preservation along the AI lifecycles. In this paper, we propose a blockchain based framework for trustful AI and traceable end-to-end training and inference. The framework leverages a distributed ledger to immutably bind the hash-locked commitment of data and model parameters through deployed smart contracts defining automated verification protocols. Secure multiparty computation [PADDP13] and zero-knowledge proofs are some of the privacy mechanisms used during collaborative training in federated learning environments to keep sensitive data safe. In expanding on our work in blockchain-based AI integrity frameworks, the new architecture shown integrates anomaly detection and provenance tracking to prevent malicious contributions. Extensive experimental evaluations over standard datasets confirm the ability of our solution to efficiently provide reliable verification without additional computational background for domain independence. This work has the potential to form the bedrock of future AI deployments that are transparent, safe, and ethically responsible.
Blockchain technology offers an immutable record of verified information, which enables its participants to exchange data in a trustless environment. However, providing at once the properties of integrity, verifiability, availability raises challenges in scenarios where data confidentiality must be preserved. While techniques such as data anchoring, zero-knowledge proofs, or homomorphic encryption have been proposed to address these challenges, formalizing their uses in the context of blockchains, into accessible design patterns for non-expert audiences remains underexplored. This paper proposes a comprehensive collection of blockchain patterns addressing confidentiality-related use cases. The patterns are organized into three families: (1) patterns for confidential data sharing; (2) patterns for claim management—including proof issuance and verification—originally introduced in a previous paper and revised herein; and (3) patterns for secure computation over private inputs. This collection provides a conceptual framework that structures and unifies emerging approaches in this fast-evolving area, laying the groundwork for future standardization and implementation efforts. It offers actionable insights for practitioners, combining best practices with architectural guidance for safeguarding data within blockchain systems.
Extends the EQBSL (Evidence-Quality Bayesian Subjective Logic) framework with zero-knowledge proof constraints. Allows a prover to demonstrate that their trust opinion was computed correctly from private evidence, without revealing the evidence itself. Bridges cryptographic privacy guarantees with the epistemic trust formalism of EQBSL.
As soon as supply chain management is done well in online marketplaces, it will lead to better logistics, lower costs, and satisfied customers. Customers are also happier. When Blockchain technology and Federated Learning (FL) are used together, they could make things safer. demand estimates that are accurate and the protection of personal information are both important. There is a lack of trust between stakeholders, data silos, and the possibility of cyberattacks, which are some of the difficulties that are associated with the supply chain management systems of today. By utilizing these models, individuals are able to make educated estimations regarding the amount of demand that will be there in the future. For the purpose of addressing these concerns, we suggest using a technique known as Blockchain-Based Decentralized Federated Learning (BC-DFL), which is a mechanism for forecasting demand. Using the immutable ledger that blockchain provides, this technology ensures the secure transportation of data. Having the ability to see demand in real time, providing members of the supply chain with increased trust, and requiring less assistance from third-party aggregators are just some of the many benefits that come with the solution that is proposed. Based on the findings, it is clear that the BC-DFL paradigm leads in an increase in demand. With five to ten nodes with fifty thousand to one hundred thousand data, the BC-DFL architecture may be able to achieve fifty rounds of proof-of-stake consensus and AES-256 encryption. The mean absolute error was 7.6, the root mean square error was 12.9, the R2 value was 0.91, and less than 5% of the data escape.
Shaista Ashraf Farooqi, Aedah Abd Rahman Rahman, Amna Saad
The growing integration of the Internet of Medical Things (IoMT) into healthcare has amplified the need for secure and privacy-preserving artificial intelligence. Federated Learning (FL) has emerged as a pivotal paradigm for decentralized medical data processing; however, it still faces challenges concerning data confidentiality, trust management, and scalability. This review presents an extended theoretical comparison of two prominent privacy-preserving frameworks—Federated Learning with Differential Privacy (FL-DP) and Federated Learning with Blockchain (FL-BC)—to assess their suitability for ensuring data security, transparency, and regulatory compliance in IoMT environments. The FL-DP framework safeguards patient data through noise injection during model updates, offering mathematically proven privacy guarantees. Conversely, the FL-BC framework reinforces trust and integrity via immutable ledgers and consensus mechanisms such as Proof of Stake (PoS) and Byzantine Fault Tolerance (BFT). Reviewing literature published between 2021 and 2025, this study examines trade-offs in privacy, scalability, latency, and energy efficiency, while highlighting emerging hybrid architectures that integrate both approaches. The findings reveal that FL-DP provides stronger privacy control, whereas FL-BC ensures verifiable trust and traceability—together forming the foundation for next-generation secure and trustworthy federated learning systems in IoMT-driven healthcare.
The fast growth of Internet of Things (IoT) technologies has turned smart cities into big data ecosystems for intelligent mobility, energetic efficiency and public services. But this increasing reliance on IoT data raises significant privacy issues because of the perpetually gathered sensor readings, inter-organisational sharing and algorithmic analyses. In this paper, we focus on the state-of-the-art IoT data-sharing methods that preserve privacy and protect recent progress in preserving privacy while sharing data in the IoT personal record by preserving statistical value. It combines traditional approaches, including anonymisation, differential privacy, federated learning, secure multiparty computation and homomorphic encryption with new technologies (e.g., blockchain-enabled governance, edge intelligence or zero-knowledge proofs) (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The paper analyses the impact of hybrid architectures combining edge-cloud cooperation and decentralised access control for improving data protection, in terms of not losing performance or interoperability. Conclusions: Summary of the main findings, Barriers to Implementation. This paper identifies several ongoing barriers, including computational expense, related to past research. Personal data while preserving its analytical worth. It combines cutting-edge technologies like blockchain-enabled governance, edge intelligence, and zero-knowledge proofs with traditional strategies like anonymisation, differential privacy, federated learning, secure multiparty computation, and homomorphic encryption (Nguyen et al., 2023; Alrawais et al., 2024; Lin & Kuo, 2025). The study investigates how hybrid architectures that incorporate decentralised access control and edge-cloud collaboration can improve data security without compromising interoperability or performance. The results point to enduring obstacles, such as interoperability, computational overhead, and regulatory compliance, especially in urban settings with limited resources. In order to integrate privacy-by-design principles into IoT analytics for smart city governance, the study suggests a multi-layered conceptual framework. To maintain public confidence in urban digital transformation, this framework places a strong emphasis on open data policies, citizen consent procedures, and the incorporation of cutting-edge cryptographic techniques. The information adds to the current discussion on how to balance privacy and innovation in smart cities and provides guidance for system architects, legislators, and municipal IT leaders who want to adopt IoT responsibly.
Healthcare collaborative processes still encounter major challenges, particularly regarding the interoperability of heterogeneous information systems, the traceability of medical interventions, and the secure sharing of patient data under strict privacy regulations such as the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). This paper presents a patient-centric, blockchain-based framework designed to overcome these limitations. The proposed solution integrates smart contracts and non-fungible tokens (NFTs) within the Ethereum blockchain to ensure data integrity, traceability, and privacy preservation. Furthermore, a compliance-by-design mechanism is embedded into the smart contracts to enable self-supervision of collaborative workflows without third-party intervention. A Proof-of-Authority (PoA) consensus protocol is also adopted to optimize validation efficiency and significantly reduce computational and energy costs.
Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topologies, several limitations remain: scalability challenges with large agent populations, communication overhead, lack of privacy guarantees, and suboptimal resource allocation. We propose AgentNet++, a hierarchical decentralized framework that extends AgentNet with multilevel agent organization, privacy-preserving knowledge sharing via differential privacy and secure aggregation, adaptive resource management, and theoretical convergence guarantees. Our approach introduces cluster-based hierarchies where agents self-organize into specialized groups, enabling efficient task routing and knowledge distillation while maintaining full decentralization. We provide formal analysis of convergence properties and privacy bounds, and demonstrate through extensive experiments on complex multi-agent tasks that AgentNet++ achieves 23% higher task completion rates, 40% reduction in communication overhead, and maintains strong privacy guarantees compared to AgentNet and other baselines. Our framework scales effectively to 1000+ agents while preserving the emergent intelligence properties of the original AgentNet.
The union of Blockchain and Federated Learning (FL) technologies in the Internet of Vehicles (IoV) domain has ushered in new possibilities for privacy-preserving, decentralized, and cyber-resilient financial use cases. As autonomous and connected vehicles (CAVs) are integrated with edge computing and 5G/6G networks, these smart nodes increasingly engage in real-time financial transactions spanning from usage-based insurance, automated tolls, electric vehicle charging fee payments, to intelligent vehicle leasing and decentralized vehicular identity management. Such a dynamic scenario is best addressed by Blockchain technology, which provides a decentralized ledger system that promises immutability, transparency, and trust, which are perfect for authenticating transactions like insurance claims, micropayments, and contractual fulfilment in mobility finance. This enables IoV stakeholders to build predictive systems with data sovereignty and low latency. Even so, the joint employment of blockchain and FL in financial environments poses important regulatory and ethical challenges.
In today's digital age, cloud storage and computing have become indispensable. Resource-constrained clients such as individuals and small organisations increasingly rely on powerful servers to store, manage and process their data. However, outsourcing data to external servers leads to significant privacy concerns, particularly when dealing with sensitive information such as medical records, financial transactions, or personal data. Fully homomorphic encryption (FHE) is a cryptographic technique that allows computation over encrypted data. In secure outsourcing with FHE, a client sends encrypted data to a server, which can perform requested computations without accessing the original data. The server returns the resulting ciphertexts, which the client can decrypt to obtain the final output. Despite its strong privacy guarantees, the practical adoption of FHE is limited by two main challenges: efficiency, which arises from the substantial performance overhead of FHE; and integrity, which stems from the lack of mechanisms to verify the correctness of the outsourced computation. In this thesis, we contribute to addressing these challenges in three aspects. First, we optimise oblivious algorithms for use in FHE, achieving improvements in key performance metrics and accelerating both bootstrapping and a range of applications. Second, we build efficient privacy-preserving information systems based on FHE. These include (i) two private machine learning protocols, the k-nearest neighbour algorithm and decision tree evaluation, (ii) SQUID, a secure system for storing and analysing genotype-phenotype data, and (iii) a protocol for securely delegating zero-knowledge proof generation. Third, we construct verifiable secure delegation of computation through FHE techniques. We provide the notion of blind proofs to provide integrity guarantees and demonstrate its practicality using blind zkSNARKs, a concrete instantiation of blind proofs.
P. Prakash, Faheema Kattakath Sanil, Jeffrey Tom Shaji, Saravanan Palani · 5 authors
The adoption of privacy-preserving techniques in healthcare is significant, especially while handling sensitive medical information. Traditional machine learning approaches raise significant concern regarding privacy, regulations, and data protection. Federated learning has emerged as an effective machine learning approach that enables a group of local models to collaboratively train the global model by sharing their updates instead of sharing the sensitive medical data. Nevertheless, a significant issue with federated learning is its vulnerability to various attacks, including model corruption and data tampering. The authors propose a methodology for developing a secure and privacy-safeguarded collaborative learning model by integrating zero knowledge proof (ZKP) with federated learning (FL). The proposed RS-ZKP methodology utilizes Pedersen commitments within ZKP to verify feature importance, ensuring that they fall within specified bounds without disclosing the actual values. The methodology is validated on two benchmark datasets using metrics accuracy, precision, recall, and F1 score.
With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.
The protection of the privacy, integrity, and auditability of big data has emerged as a significant concern as organizations are growing more dependent on multi-cloud data ecosystems which are decentralized in nature. The problems that traditional data governance models are not able to deal with are a lack of transparency, distrust towards information sharing, inconsistent access control, and their tendency to be tampered with. In order to close these shortcomings, the paper presents a Privacy-Aware Big Data Governance Framework (PBGF) which uses blockchain technology, distributed access control, and fine-grained privacy preservation to safeguard the total data lifecycle. The framework has incorporated smart contracts to automate governance policies, decentralized identity to ensure security in the authentication process, and differential privacy to safeguard sensitive attributes in analytics. A multilayer architecture is created to make sure that there is trusted data ingestion, provenance tracking, compliance, and verifiable data use across the stakeholders. Experimental considered performances indicate better data integrity, traceability, and accountability of access with minimal overhead in analytics performance. The solution is a scalable, tamper-resistant, and privacy-compliant governance model that proposed can be used in large big data systems in the fields of finance, healthcare, and smart cities.
Jiaxi Liu, Lin Sun, Tianyu Kang, Di Wu · 7 authors
Federated Learning (FL) enables model training on distributed devices while preserving data privacy. However, malicious clients can submit fabricated model updates to fraudulently obtain training rewards, a behavior known as free-rider attacks. Existing detection-based solutions analyze anomalies in model updates but lack direct evidence of local training, making it fail to fully prevent free-riders. To address this limitation, we propose zkVFL, a verifiable FL framework leveraging Zero-Knowledge Proofs (ZKP) to ensure the integrity of local training while preserving privacy. To reduce the computational overhead of proof generation in ZKP, zkVFL introduces two novel techniques: (i) anomaly-aware client sampling to selectively perform ZKP verification and (ii) A recursive ZKP protocol (ReMPoT), incorporating a pruning-based layer selection technique, reduces proof generation costs. Experimental results demonstrate that zkVFL improves the accuracy and convergence of FL training under free-rider attacks while significantly reducing the computational and memory overhead of proof generation on resource-constrained devices.
P. Bhuvaneshwari, A Krishnaveni, Harold Robinson, E. Golden Julie
The deep learning technique has emerged as an exemplary model for managing the Artificial Intelligence-based Blockchain framework with technological enhancements to guarantee reliable data through the consensus procedure. The deep learning-enabled blockchain transaction model has involved the development of security to solve the problems of confidentiality and data anonymity. The Hybrid techniques of the Blockchain with the Deep Learning technique are proposed to generate enhanced data durability and its propagation through the enhanced convolutional temporal network (EnCTN) for transaction analysis in a blockchain-enabled Auto Encoder technique. The sliding window extraction technique is used to extract information from a particular window size to evaluate the needed input values from the temporal series. The dilated Convolution is used to capture the long-range dependencies. The proposed technique is implemented in the Ethereum environment using Python, and experimental results show that it has produced an improved performance than the relevant technique in several performance parameters. The anomaly classification accuracy is improved than the relevant technique and it is evaluated using the NSL-KDD dataset. The proposed framework delivers an efficient solution for the real-world anomaly detection application while accurate discovery of temporal anomalies and computational efficiency is enhanced.
Privacy-preserving computation enables multiple parties to jointly compute a function while keeping their inputs private. Protocols designed for the semi-honest model achieve high efficiency by assuming participants will correctly follow the protocol’s cryptographic steps. However, this security assumption is confined to the protocol’s internal execution, creating a crucial accountability gap. It offers no inherent method to prove that the inputs and function used in the computation actually align with what was externally agreed upon. In this paper, we introduce a novel framework that enhances privacy-preserving computation with public verifiability and accountability, while maintaining composability. Our framework leverages a blockchain as an immutable trust anchor to register cryptographic commitments of both participant inputs and the function’s specification. We then employ a zero-knowledge proof protocol to verify that the privacy-preserving computation is performed correctly using the committed data and function logic. The security of our model is formally proven to guarantee both input privacy and computational integrity, while our performance evaluation shows its practical scalability.
Decentralized anonymous credentials (DACs) enable users to prove possession of specific identity attributes without disclosing additional information or relying on a centralized authority. However, existing DAC schemes commonly rely on complex zero-knowledge proofs, resulting in high computational overhead. They also lack sufficient flexibility and efficiency in handling multi-authority environments and supporting complex access policies, while facing limitations in trust assumptions and scalability. To address these challenges, this paper proposes a novel threshold anonymous credential scheme. Specifically, we introduce a ciphertext-policy attribute-based encryption (CP-ABE) scheme that supports threshold key distribution and aggregation, and leverage it to construct the threshold anonymous credential scheme. Experimental results and security analysis demonstrate that the proposed scheme exhibits high efficiency and flexibility in constructing authentication for complex access policies.
Hessah A. Alsalamah, Saeed Alqahtani, Ghazlan Al-Arifi, Jana Al-Sadhan · 8 authors
Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems.
Graph-structured data has become central to modern analytics, enabling institutions to model relationships in domains such as healthcare, finance, cyber security, and education. However, privacy regulations and institutional policies restrict the sharing of sensitive nodes, edges, or interaction logs, preventing the discovery of global graph patterns. This paper introduces a novel framework for Federated Graph Pattern Mining Across Institutions (FGPM-AI), enabling multiple organizations to collaboratively extract global sub graphs, motifs, and temporal patterns without sharing raw graph data. The framework proposes six novel contributions: (1) Privacy-Preserving Pattern Signatures (PPPS) for anonymized sub graph encoding, (2) Federated Temporal Graph Pattern Mining (FT-GPM) to learn evolving patterns across distributed graphs, (3) Zero-Exchange Federated Sub graph Matching (ZE-FSM) using zero-knowledge proofs, (4) Heterogeneity-Aware Graph Pattern Consensus (HGPC) for semantic alignment between distinct graph schemas, (5) Communication-Adaptive Pattern Sharing (CA-FGM) for bandwidth-efficient collaboration, and (6) Multi-Party Graph Pattern Distillation (MGPD) for merging patterns into a unified knowledge model. Experimental design considerations demonstrate the feasibility and robustness of the framework. The results highlight FGPM-AI as a promising direction for secure, scalable, and intelligent cross-institution graph analytics.
Data protection in smart agricultural markets remains a critical area of inquiry amid escalating digital transformations. This chapter examines the challenges and solutions surrounding privacy in agricultural data ecosystems, with particular attention to encryption, blockchain, and federated learning technologies. It also evaluates global legal, ethical, and regulatory frameworks such as the GDPR and CCPA that influence data governance in digital agriculture. As of 2023, over 75% of large-scale farms in developed nations employ IoT and AI-driven tools for data collection, yet only 38% have robust data protection measures in place, exposing vulnerabilities in ownership, consent, and access control. The integration of privacy-preserving mechanisms such as homomorphic encryption, zero-knowledge proofs, and secure multi-party computation is discussed as a pathway to sustainable data security. The findings emphasize the urgent need for interoperable standards and policy-driven frameworks to protect stakeholders, particularly smallholder farmers, within smart agricultural marketplaces.
Istiaque Ahmed, Tadashi Nakano, Kentaroh Toyoda, Thi Hong Tran
Digital identity verification has become crucial to every service in daily life. The privacy concerns associated with traditional Know Your Customer (KYC) systems have come to the forefront. These systems often require the sharing of personal information, which is stored in centralized databases, making them vulnerable to unauthorized access. To address these challenges, this work implements an electronic KYC system with selective disclosure using Merkle Tree and Zero-Knowledge Proofs (ZKP). Selective disclosure enables users to share only the necessary information, thereby reducing the exposure of sensitive data. ZKP enables the verification of this information without revealing the actual data, ensuring that privacy is preserved. The combination of selective disclosure and zkSNARKs in the proposed framework provides a solution for generating a single proof compared to multiple market proofs. This work demonstrates significant improvements in privacy protection compared to traditional identification systems. The implementation process and performance evaluation explore its potential impact on eKYC.
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributors’ historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing.
Riku Miyake, Toru NAKANISHI, Teruaki Kitasuka, Zhuotao Lian
Although current digital identity systems are centralized, decentralized systems based on Verifiable Credentials (VCs) are gaining attention and moving towards practical implementation. As one of VC systems, a VC system with selective disclosure has been proposed, where credentials are represented as directed graph based on the concept of Linked Data (LD). However, in the existing VC system, the verification time increases depending on the number of RDF terms that correspond to vertices and edges in the proved graph, due to the characteristics of the utilized signature scheme. Meanwhile, a zero-knowledge proof system for directed graphs using a pairing-based accumulator has been proposed. This system is characterized by its verification time and proof data size being independent of the number of vertices and edges in the graph. In this paper, we propose a LD-based VC system with selective disclosure that leverages the zero-knowledge proof system on graph; the verification time and proof size are independent of the number of vertices and edges. Furthermore, we reduce the proof data size by modifying the signature scheme from AHO signatures to SPS-EQ signatures and from the pairing-based accumulator to a set commitment. We implement and evaluate the proposed system on a PC.