Blockchain Papers

UmmeAmmara Qureshi, Bhumika Doshi, Aditya More, Kashyap Joshi · 5 authors

Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs...

Elif Nur Kucur, Tolga Büyüktanır, Muharrem Ugurelli, Kazım Yıldız

Privacy-preserving machine learning (PPML) constitutes a core element of responsible AI by supporting model training and inference without exposing sensitive information. This survey presents a comprehensive examination of the major cryptographic PPML techniques and introduces a unified taxonomy covering technical models, verification criteria, and evaluation dimensions. The study consolidates findings from both survey and experimental works using structured comparison tables and emphasizes that recent research increasingly adopts hybrid and verifiable PPML designs. In addition, we map PPML applications across domains such as healthcare, finance, Internet of Things (IoT), and edge systems, indicating that cryptographic approaches are progressively transitioning from theoretical constructs to deployable solutions. Finally, the survey outlines emerging trends—including the growth of zero-knowledge proofs (ZKPs)-based verification and domain-specific hybrid architectures—and identifies practical considerations that shape PPML adoption in real systems.

Savvy Sharma, George Petrovic, Sarthak Kaushik

Healthcare AI needs large, diverse datasets, yet strict privacy and governance constraints prevent raw data sharing across institutions. Federated learning (FL) mitigates this by training where data reside and exchanging only model updates, but practical deployments still face two core risks: (1) privacy leakage via gradients or updates (membership inference, gradient inversion) and (2) trust in the aggregator, a single point of failure that can drop, alter, or inject contributions undetected. We present zkFL-Health, an architecture that combines FL with zero-knowledge proofs (ZKPs) and Trusted Execution Environments (TEEs) to deliver privacy-preserving, verifiably correct collaborative training for medical AI. Clients locally train and commit their updates; the aggregator operates within a TEE to compute the global update and produces a succinct ZK proof (via Halo2/Nova) that it used exactly the committed inputs and the correct aggregation rule, without revealing any client update to the host. Verifier nodes validate the proof and record cryptographic commitments on-chain, providing an immutable audit trail and removing the need to trust any single party. We outline system and threat models tailored to healthcare, the zkFL-Health protocol, security/privacy guarantees, and a performance evaluation plan spanning accuracy, privacy risk, latency, and cost. This framework enables multi-institutional medical AI with strong confidentiality, integrity, and auditability, key properties for clinical adoption and regulatory compliance.

Kavitha A Karkera, J P Patra

Rapid digital transformation across financial, e-commerce, and decentralized platforms has amplified the need for secure, transparent, and resilient transaction systems. Conventional security mechanisms often fail to address sophisticated cyber threats, identity fraud, and evolving attack patterns, highlighting the necessity for integrated technological solutions. The convergence of Blockchain and Artificial Intelligence (AI) establishes a robust framework that combines immutable, decentralized ledger structures with adaptive, intelligent analytics. Blockchain ensures transactional integrity, data provenance, and decentralized trust, while AI facilitates real-time anomaly detection, predictive risk scoring, and automated decision-making. This synergy enhances digital identity management, strengthens access control, and mitigates fraud by enabling continuous monitoring, behavioral analysis, and transparent verification processes. Applications extend to secure payments, smart contracts, cross-border transactions, and decentralized finance ecosystems, demonstrating improved operational efficiency, scalability, and resilience. The chapter also explores privacy-preserving computation, federated learning, and explainable AI frameworks to ensure ethical and accountable deployment of intelligent transaction systems. By integrating structural security with predictive intelligence, Blockchain-aided AI frameworks establish a next-generation foundation for secure digital transactions, fostering trust, regulatory compliance, and systemic reliability across global digital networks.

Saad Alaklabi

Financial institutions are currently faced with suffering never experienced before as they strive to guarantee the privacy of data and address the demands of regulation to report and cooperate in machine learning. This paper proposes PrivChain-AI, a novel blockchain-based federated learning system designed to facilitate secure and privacy-preserving financial reporting and access control. The proposed framework will integrate three key components: differential privacy, homomorphic encryption, and smart contract-based governance, enabling cooperative model training across financial institutions while preventing the leakage of sensitive information. PrivChain-AI is a hierarchical design that incorporates permissioned consensus protocols and utilises zero-knowledge proof verification to authenticate transactions. It has been demonstrated that the performance is higher than that of the actual financial data, with an outcome of 94.7% accuracy in fraud recognition at the cost of e-differentiation privacy, where ϵ = 1.0. It is 40% faster in terms of communication overhead and ensures regulatory compliance, as it features immutable audit trails. The analysis of performances reveals that a privacy preservation metric improves by 78%, and access control granularity is improved by 62% compared to the current state-of-the-art approaches. The PrivChain-AI paradigm introduced provides a new analytical model for safe, collaborative finance, meeting the highest standards and ensuring compliance with relevant regulatory jurisdictions.

Damilare E. Bakare, Adekemi Olawunmi Amoo, Mary T. Onifade

The health insurance sector has been facing many challenges recently, such as fraudulent activities in insurance claims, data breaches, and high transaction costs, particularly with existing systems built on the Ethereum network, which negatively affect its efficiency and effectiveness.These challenges undermine the trust and financials of insurance providers while compromising the privacy of the patient's health records.To address this issue, this study proposes a conceptual framework that uses zero-knowledge proof within the blockchain system and is deployed on the Polygon Network for its low transaction fees and higher throughput.The proposed model allows the verification of an insurance claim without revealing sensitive patient health records, ensuring privacy while preventing fraudulent activities.In this conceptual design, the hospital can issue verifiable proof of treatment, appointment, and bill that shows the validity of the insurance claim without revealing the underlying health record to the insurer.This study, therefore, contributes to supporting research in decentralized applications for healthcare insurance by presenting a conceptual model and comprehensively analyzing the feasibility, rather than a full-scale implementation.It also emphasizes the need to preserve privacy in sensitive domains and the potential benefits of blockchain and ZKP integration.In conclusion, the research's findings show that, in theory, integrating ZKP with blockchain technology can enhance healthcare insurance processes in terms of reliability, efficiency, privacy, and security.However, further research and practical development are required to realize and evaluate a fully operational system.

Licheng Lin, Bin Feng, Pujie Jing

With the rapid development of geological blockchains and Internet of Things-based data acquisition technologies, massive amounts of heterogeneous data are constantly emerging. However, this data is stored in a distributed manner across different organizational or business blockchains. Data sharing among multiple geological blockchains faces numerous challenges, either exposing sensitive data during verification or lacking effective authorization mechanisms. Therefore, how to achieve fine-grained access control and privacy protection across multiple blockchains has become a critical issue that must be addressed in geological data sharing. In this paper, we propose GeoCross, a cross-chain geological data sharing framework that enables fine-grained authorization management and privacy protection. First, GeoCross provides a hierarchical hybrid encryption mechanism that uses symmetric encryption for geological data protection and ciphertext-policy attribute-based encryption to enable flexible cross-chain access policies. Second, we integrate a Groth16-based zero-knowledge proof mechanism, which allows a chain to verify the existence, integrity, and accessibility of off-chain data without revealing the content. Furthermore, we introduce a Reputation-based Non-interactive Relay node Selection protocol (RNRS), which enhances the trustworthiness and fairness of cross-chain routing. Finally, we implement GeoCross in a multi-chain Hyperledger Fabric environment and evaluate its performance under real-world workloads. Results show that Groth16 verification requires only three bilinear pairings, achieving a throughput of up to 390 tps on a single chain and 1550 tps in a concurrent multi-chain environment. Even with 50% malicious nodes, the RNRS protocol still maintains a success rate of over 91%. These results demonstrate that GeoCross provides an efficient and practical solution for secure and privacy-preserving cross-chain geological data sharing.

MAZARI, Ilyes Tarik, Mazari, Yanis, Ilyan, Mazari

We present the Y.I.N. Mazari Architecture, an 8-pillar privacy-preserving federated learning system built around a novel cryptographic ordering: DP→ZK→HE (Differential Privacy →Zero-Knowledge Proof →Homomorphic Encryption) applied to federated learning gradients. The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, while embodying the core principle that Your Information Never leaves your control.We identify a fundamental barrier in privacy-preserving federated learning: the inability to verify that participants correctly applied differential privacy noise while maintainin computational efficiency. The Y.I.N. Mazari Ordering resolves this barrier through a specific sequencing of cryptographic operations.This paper extends the classical architecture into the quantum domain through the QFED-MAZARI system,introducing the Mazari Quantum Ordering: QDP→MUA→DQEM(Quantum Differential Privacy →Manifold Unitary Aggregation →Distributed Quantum Error Mitigation). Experimental results demonstrate 99.37% model accuracy with 223× speed improvement in classical systems, while the quantum extension achieves 91.9% accuracy with 40–50% communication reduction. Together, the classical and quantum architectures establish a comprehensive 30-year intellectual property runway.

Mallikarjun Reddy Gouni

Financial crime detection faces unparalleled challenges as criminal networks exploit digital payment channels, cryptocurrency platforms, and cross-border transaction systems outside traditional monitoring frameworks. In this respect, AFCI introduces a novel framework for federated machine learning, regulatory reasoning engines, and real-time risk propagation analytics to build unified global privacy-preserving anti-crime intelligence ecosystems. The framework lets organizations train collaborative models with decentralized institutions, safely aggregating information from multiple parties without sharing sensitive transaction data by means of secure aggregation protocols and differential privacy mechanisms. Large language models coupled with knowledge graphs automate the processes of regulatory interpretation and rule generation, and graph neural networks enable the detection of coordinated criminal activities on a large scale in transaction networks through temporal message passing mechanisms. Reinforcement learning agents continuously optimize detection policies to balance the identification of genuine threats against the goal of minimizing false alarms. The framework bridged critical gaps in cross-border compliance coordination and empowered institutions to develop shared detection capabilities in support of data localization requirements and an array of diverse regulatory frameworks. Long-term security of privacy-preserving federated computation would be guaranteed with post-quantum cryptography. This convergence of advanced technologies allows next-generation financial crime prevention systems to remain effective against evolving criminal methodologies while preserving fundamental privacy rights.

Tan Gürpinar, Mehmet Akif Gulum, Melanie Martinelli

Enterprises today face increasing threats from cyberattacks, supply chain disruptions, and systemic market risks, making the enhancement of organizational resilience through advanced risk management frameworks increasingly critical. Traditional approaches often struggle to balance data privacy, cross-organizational collaboration, and real-time adaptability. While distributed ledger technologies (DLTs) initially enabled cryptocurrencies, they have evolved into a foundational infrastructure for decentralized AI applications. This study investigates how decentralized AI techniques, particularly federated learning, can support joint risk management processes in enterprise networks. First, a comprehensive review of decentralized AI methods is conducted to identify approaches suitable for enterprise risk management. Next, expert interviews are used to contextualize these insights, highlighting practical considerations, organizational challenges, and adoption constraints. Building on the literature and expert feedback, a decentralized framework is developed to allow organizations to securely share risk-related insights while preserving data privacy and control over proprietary information. The framework is validated through a technical prototype, combining architectural design with empirical proof-of-concept experiments on federated learning benchmarks. Results demonstrate the feasibility of achieving near-centralized model accuracy under privacy constraints, while also highlighting communication and governance issues that need to be addressed in real-world deployments. The study presents a structured comparison of decentralized AI techniques and a validated concept for enhancing supply chain risk prediction, fraud detection, and operational continuity across enterprise networks.

Li Yinfeng

In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.

Vinoth Punniyamoorthy, Ashok Gadi Parthi, Mayilsamy Palanigounder, Ravi Kiran Kodali · 6 authors

Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.

Kadhim Abdulfadhil Gatea, Ehsan Shoja, Parviz Rashidi Khazaee, Hossein Nahid-Titkanlue

The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.

Mazari, Ilyes Tarik

This document provides a comprehensive prior art disclosure for the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The Y.I.N. Mazari Ordering establishes that for efficient cryptographic verification of differential privacy compliance, zero-knowledge proofs must be generated before encryption, not after. This disclosure documents extensions, variations, and applications of the ordering across: (1) all cryptographic primitives including post-quantum schemes, (2) all zero-knowledge proof systems, (3) diverse application domains including financial services, healthcare, and emerging technologies, and (4) various architectural configurations and trust models. The disclosure is published in the spirit of scientific contribution while establishing prior art for the described variations. Associated patent applications: U.S. Provisional Patent No. 63/923,348, U.S. Patent Application No. 19/399,646, and U.S. Continuation Application No. 19/403,244. Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Y.I.N. Mazari Ordering, Privacy-Preserving Machine Learning, Prior Art Disclosure

Sathya Krishnasamy

Abstract Background: Healthcare organizations face unprecedented challenges in maintaining process compliance due to increasingly federated data and systems topologies, coupled with complex state, federal, and jurisdictional regulatory compliance and verification requirements. The emergence of distributed ledger technology (DLT) and artificial intelligence presents both transformative opportunities and significant compliance challenges. These emerging technologies enable computing paradigms that shift toward data locality models where computational models meet the data rather than moving sensitive patient information across organizational boundaries. This computational approach offers innovative pathways to mitigate data breach risks, while simultaneously introducing new verification complexities as the underlying technologies continue to advance: healthcare entities must cryptographically prove that operations performed on locally-held data were executed according to approved specifications while enabling selective disclosure capabilities across entity lines. However, traditional verification mechanisms lack the cryptographic guarantees necessary for these privacy-preserving, multi-entity healthcare workflows, creating substantial risks in clinical decision-making, patient privacy, and regulatory adherence. Objective: This paper introduces the ZK-PRET Business Process Prover framework that integrates Object Management Group (OMG) business process standards with zero-knowledge cryptographic verification to enable privacy-preserving healthcare process compliance across distributed systems. Methods: We developed a multi-layer architecture combining formal business process modeling, zero-knowledge proof generation, and regulatory compliance verification. The framework extends established OMG standards with cryptographic verification capabilities to achieve verifiable compliance, privacy preservation, and regulatory accountability. Implementation testing was conducted in synthetic data environments designed to represent real-world healthcare scenarios.¹ These environments enable comprehensive modeling and testing of multi-entity process orchestration patterns while maintaining privacy protections essential for healthcare research and development. All scenarios, clinical examples, and process expressions presented in this paper utilize synthetic data to ensure no real patient data, clinical records, or identifiable health information was used. Results: The ZK-PRET Business Process Prover framework demonstrates practical applicability across many healthcare domains including treatment planning, telemedicine coordination, healthcare administration, consumer health services, multi-entity clinical trials, and supply chain management. Implementation results demonstrate cryptographic verification capabilities that enable mathematical prevention of regulatory violations rather than post-hoc detection. The results demonstrate configurable privacy preservation through zero-knowledge verification and consistent proof sizes suitable for modeling complex orchestrations, while leveraging already widely used Web 2 process models, suitable for multiple runtime deployment topologies. Conclusions: Zero-knowledge healthcare process verification represents a foundational technology for regulatory compliance in distributed healthcare systems. While agentic AI systems present important opportunities for automation, the underlying requirement for verifiable process compliance through cryptographic means brings broader challenges. ZK-PRET Business Process Prover addresses these challenges in healthcare transformative flows, enabling safer deployment of autonomous systems while maintaining regulatory standards.

Nandini K, Giris Shivappa, Sharon Zachariah, Thanushree B.Tech Thanushree B.Tech · 8 authors

Genomic data sharing remains a core problem in precision medicine because genomic data are highly sensitive and unchangeable. In this article, we propose a blockchain-based framework that utilizes zero-knowledge proofs (ZKPs), smart contracts, and off-chain storage to facilitate secure, privacy-preserving data sharing within health record systems. We implemented and evaluated a proof-of-concept prototype in Python on a simulated genomic dataset. The prototype uses a hybrid storage system where metadata is retained on a blockchain and encrypted data are placed in an emulated InterPlanetary File System (IPFS). Rule-based access is controlled using smart contracts, while privacy and security are achieved using ZKPs with interactive Schnorr protocol and elliptic curve cryptography (ECC). Empirical analysis using real-time testing over 100 iterations reported an average zero-knowledge proof with blockchain (ZKPB) query latency of 5.83 ms with a 90.00% accuracy, smart contract latency of under 0.01 ms with 90.00% accuracy, blockchain query time of 0.01 ms with 90.00% accuracy, and ECC latency of 8.72 ms with 90.00% accuracy. These empirical findings validate the effectiveness and privacy guarantees of the framework, which can be utilized in healthcare research, clinical genomics, and personalized medicine workflows.

Dan Wang, Ying Wang

Sample alignment performs a crucial role in vertical federated learning, aiming to identify shared user samples among multiple parties without exposing their private identifier data. However, most existing alignment protocols are designed for two-party scenarios, while those developed for multi-party settings suffer from limited anti-collusion capability and inefficient verification mechanisms. To address these issues, we propose an efficient and secure protocol for sample alignment in multi-party vertical federated learning (MESA). The protocol leverages a threshold oblivious pseudo-random function (T-OPRF) combined with a distributed key generation scheme to defend against collusion attacks. Moreover, an oblivious key–value store encoding (OKVS) mechanism is introduced to enable secure and efficient key–value mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.

Zhaoyu Wang, Pingchuan Ma, Zhantong Xue, Yanbo Dai · 6 authors

Prescriptive analytics seeks to identify optimal interventions for achieving desired outcomes, with causal inference playing a pivotal role in assessing intervention impacts on complex systems. However, existing approaches frequently neglect critical data privacy considerations and provide no means to verify the integrity of their recommendations. These limitations hinder its adoption in high-stakes domains such as healthcare and finance. In this paper, we introduce, zkCLEAR, a zero-knowledge proof (ZKP)-based C ausal Inference ( LEA rning and R easoning) framework for privacy-preserving and verifiable prescriptive analytics. Our solution allows data owners or service providers to cryptographically prove the validity of prescriptive conclusions derived from causal analysis without disclosing sensitive source data or proprietary causal models. We develop a suite of ZKP-friendly causal operators to build efficient causal modules, including structure learning, parameter learning, probabilistic inference, and counterfactual reasoning. To optimize performance, we also introduce a workflow decomposition strategy to facilitate efficient proof generation for complex workloads. We demonstrate the utility of zkCLEAR through three real-world applications. The framework faithfully follows the behavior of non-ZKP counterparts, with moderate overheads for privacy and verifiability. Additionally, we evaluate its efficiency and scalability using real-world datasets. It shows up to a 35.1× speedup in proof generation time and a 214.5× reduction in proof size compared to current general-purpose ZKP systems.

Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan

We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning

Mazari, Ilyes Tarik, Mazari, Yanis, Mazari, Ilyan

We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning

Haitham Y. Adarbah, Kewei Sha, Afzel Noore

Federated learning (FL) enables collaborative model training across edge devices without centralizing raw data, but existing frameworks remain ill-equipped to support data privacy regulations mandated by GDPR, HIPAA, and CCPA. Once user data has influenced training, its verifiable removal becomes prohibitively expensive, particularly in non-IID and resource-constrained edge environments. This paper introduces a modular and scalable federated unlearning framework that unifies three complementary strategies: gradient subtraction, knowledge distillation, and checkpoint rollback, within an adaptive decision layer. A resource-aware checkpoint manager reduces storage costs through compression and pruning, while a privacy and trust layer integrates zero-knowledge proofs, differential privacy, and Merkle-based audit logs to provide verifiable guarantees of deletion. A non-IID-aware aggregator further preserves fairness across heterogeneous clients. Unlike prior approaches, our proposed framework systematically integrates rollback efficiency with formal privacy protections and auditability, offering a practical path toward trustworthy and regulation-compliant unlearning in domains such as healthcare, transportation, and smart agriculture.

Zhi-Yong Liang, Gaoyuan Liu, Ren Yi, Ming Yang · 7 authors

With the accelerated development of the global digital economy, data spaces have become a crucial infrastructure for cross-domain data circulation and value creation. However, cross-organizational and cross-regional data sharing still faces several challenges, including insufficient trust, fragmented governance, and inconsistent standards. Against this backdrop, blockchain technology, with its decentralized, traceable, and tamper-resistant characteristics, offers new avenues for building collaborative trust mechanisms within trustworthy data spaces. This paper systematically reviews the current research on trustworthy data spaces, the blockchain, zero-knowledge proofs, and federated learning. It proposes a technology-governance-standardization (TGS) framework for cross-border governance. To verify the framework, we proposed a collaborative trust mechanism combining “on-chain light attest, off-chain deep store, and cross-layer verifiable bridge” (LPHS–XV), which achieves data availability without visibility and compliance auditability. A prototype was then validated in the cross-border medical data space at the Macao-Hengqin Station, providing a scalable experience for global data governance.

Tiphaine Henry, Loïk Assekour, Alexandre Rapetti, Antonella Del Pozzo · 5 authors

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.

Oliver Hirst

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.