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.
Nur Shahidah Mohammad, Md. Mafizur Rahman, Md. Abdur Razzaque
Federated Learning (FL) enables multiple entities to collaboratively train models without sharing sensitive data, but it faces critical privacy, security, and efficiency challenges in healthcare intrusion detection systems. These issues are intensified by adversarial attacks, non-IID data, and the need for real-time performance. Existing FL methods struggle with gradient inversion, model poisoning, Sybil attacks, and high computational overhead, limiting their effectiveness in secure and scalable healthcare applications. This work proposes the PrivacyPreserving Personalized Federated Learning Intrusion Detection in Healthcare applications (P3FL-HIDS), integrating Byzantinerobust aggregation, gradient masking, and Zero-Knowledge Proof based authentication. Key features include strong adversarial resilience, protection of privacy against gradient inversion, personalized model adaptation for heterogeneous data, and secure participant authentication. Additional contributions include a dual-network training approach, adaptive clustering for personalization, and optimized secure communication for real-time healthcare scenarios. Experimental results on a Brain Tumor magnetic resonance imaging (MRI) dataset show that P3FLHIDS outperforms state of the art works in terms of accuracy, resilience, and resistance.
Blockchain is often referred to as a Distributed Ledger Technology or DLT. One could argue that there are two-components to ledger-keeping: the ledger itself (the data) and the process of âkeepingâ it or amending the data.
Federated learning (FL), which allows collaborative machine learning without requiring the centralisation of sensitive data, has become a game-changing concept for private and safe data analysis in decentralised networks. FL enables edge devices or local nodes, such as smartphones, IoT devices, or healthcare facilities, to learn shared models remotely and send only model changes to a central server, in contrast to traditional methods that call for raw data aggregation. This framework lowers communication overhead, mitigates regulatory problems, and greatly improves data privacy and security. FL provides a workable and scalable way to create superior machine learning models in decentralised networks, where data is naturally dispersed and frequently subject to stringent privacy laws. Nevertheless, there are still issues to be resolved, such as managing non-IID data, making sure that systems are resilient to hostile attacks, and preserving effective communication. To further improve FL's privacy-preserving capabilities, recent developments like homomorphic encryption, safe multiparty computation, and differential privacy are being incorporated. This study examines the fundamentals of federated learning, goes over important methods for improving its security and privacy, and talks about how it may be used in a variety of industries, including as healthcare, finance, and smart cities. FL is one of the most important steps to safe and ethical AI in decentralized environments because it allows the collaboration of intelligence and preserves data ownership.
In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.
Yi-Jing Liu, L. Zhang, Xiaoqian Li, Hongyang Du ¡ 8 authors
Integrated Sensing and Communication (ISAC) is driving the evolution of edge intelligence. In ISAC-enabled wireless edge networks, federated learning (FL) is crucial for realizing edge intelligence by supporting the networks with privacy protection, efficient data management, and dynamic adaptability. Specifically, FL allows distributed computing nodes (e.g., sensor devices) to first train local models by using data collected or sensed via ISAC and subsequently send them to one or multiple aggregation nodes for global model collaboration. However, traditional FL frameworks face significant challenges in the ISAC scenarios. For example, the privacy sensitivity of heterogeneous sensor data and the lack of transparency in model parameter exchange make it difficult to ensure the credibility of local and global models. Sharding distributed ledger technology (DLT), which divides the ledger into smaller and manageable shards, offers a potential solution to address these challenges by utilizing multi-node trust capabilities to facilitate distributed consensus during FL training. In this paper, we propose a trusted FL framework that incorporates sharding DLT within ISAC-enabled wireless edge networks to enhance both model training and consensus performance. Specifically, we develop a theoretical model to examine the interactions between model training performance and network capacities of sensing nodes (e.g., storage, computing, and communication capabilities) based on ISACâs real-time channel state information. Based on this theoretical model, we design a trusted clustering scheme for aggregating local models. Numerical results demonstrate that in ISAC-enabled wireless edge networks, our proposed scheme significantly increases network throughput for model transmission while ensuring optimal model learning performance compared to some classical baselines.
Anurag Anand Duvey, Chandrashekhar Goswami, Amit Kumar Goel
Federated Learning (FL) gives opportunity to decentralized model training without the raw data's revealing. But in actual real-world implementation faces certain number of challenges. These include trust in client updates, verifiable end-to-end privacy promises, equitable contributor compensation, and accountable aggregation. This paper provides a design-first architecture that addresses these issues by integrating concise zero-knowledge proofs (zk-SNARKs) for trustless verification with smart-contract arrangements. This approach is for verification, secure aggregate pooling, and reward settlement. Our design is consisting a structure of five-layer stack, named as Client, Proof, Blockchain, Incentive, and Governance. It highlights end-to-end workflows for the generation of proof for updates, verifying them on chain, anchor-off chain aggregation sequence anchoring, and allocate contribution-matching token payouts. We specify clearly smart-contract interfaces called as aggregation, registry, incentivization, zk-circuit objectives, and several scaling controls like proofs/aggregated in batch or Layer-2/rollup rollouts and the anchor-Merkel. The design also implements Shapley-like contribution measures and ERC-compact reward settlement. It incorporates Sybil-resistance and vesting primitives to lean against game. We define a crisp threat model, discuss security and privacy trade-offs, and suggest evaluation using healthcare and IoT benchmarks. These assess the learning utility, resilience to poisoning, fairness of the payouts, and system costs in terms of gas fee. Moreover, by training and incentivization by smart contracts at design level, we create a foundation for future prototyping and rigorous empiric testing. This sets a realistic path from the design framework to effective workable, end-to-end Privacy-preserving Federated Learning deployment.
Federated Learning (FL) enables multiple participants to collaboratively train a shared model without sharing their private data. However, FL remains vulnerable to malicious clients submitting incorrect updates to disrupt training. To address this, we formalize each clientâs local training step as a Nondeterministic Polynomial-time (NP) statement, verifiable via zero-knowledge proofs (ZKPs) at every round. We propose Jolt-FL, the first general-purpose verifiable FL framework that immediately detects and excludes malicious clients upon their first dishonest action â without relying on heuristics, statistical assumptions, or multi-round analysis. Built on Joltâs zkVM, a state-of-the-art zero-knowledge virtual machine (zkVM) developed by a16zcrypto, Jolt-FL guarantees training integrity and data privacy without trusted hardware or third-party intermediaries. By witnessing every computation step, it defends against a wide range of attack vectors, securely filtering dishonest updates even if up to 50% of clients are malicious, while preserving convergence and final model performance. To demonstrate feasibility, we implement a prototype featuring a complete end-to-end Convolutional Neural Network (CNN) for image classification using the MNIST dataset. To our knowledge, this is the first fully verifiable end-to-end CNN training under ZKPs without any custom circuit design. Our solution achieves competitive proof generation times, compact proof sizes, and low verification costsâall while preserving model accuracy on par with standard FL.
Johnny Y. Solano Marinho, Eryk Schiller, Arthur Debauge, Noria Foukia
This paper presents a framework that integrates the Coalition for Content Provenance and Authenticity (C2PA) standard with Zero-Knowledge Proofs (ZKPs), specifically the Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), to enable verifiable yet privacy-preserving authentication of digital images. Using the ZoKrates toolkit, the system derives a non-revealing fingerprint from the image, generates a succinct proof of integrity, and embeds this proof into C2PA-compliant metadata without exposing the underlying content. The proof can be verified locally or on the Ethereum blockchain using a Groth16 smart contract verifier, providing decentralized and auditably transparent validation. This capability allows journalists, victims, and legal professionals to attest to the existence and integrity of sensitive evidence while deferring its disclosure. Experimental results show that proof verification is highly efficient, requiring approximately 0.01 s, and that the entire workflow is reproducible within containerized environments. The proposed integration of zk-SNARKs with C2PA establishes a practical foundation for secure digital provenance, privacy-preserving evidence management, and strengthened trust in digital media ecosystems.
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 Ethereumâs 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.
Rafael Abreu, Alexandre Valente Sousa, LuĂs Correia, ArsĂŠnio Reis ¡ 7 authors
The integration of Artificial Intelligence (AI), Internet of Things (IoT), and Vehicle-to-Everything (V2X) technologies in green mobility systems introduces new cybersecurity and privacy challenges. This paper proposes a lightweight cybersecurity framework that integrates compact convolutional neural networks (CNNs) for real-time anomaly detection at the edge, federated learning for decentralized model training, and blockchain-based decentralized identity management with zero-knowledge proofs. These mechanisms collectively ensure sub-100 ms threat detection latency, reduced communication overhead, and GDPR-compliant privacy preservation. Simulation results demonstrate a 60% reduction in latency, 45% lower communication costs, 30% energy savings at edge nodes, and a detection accuracy of 93.4% compared to traditional cloud-centric models.
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
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.š 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.
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.
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 keyâvalue store encoding (OKVS) mechanism is introduced to enable secure and efficient keyâvalue 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.
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 35.1Ă speedup in proof generation time and a 214.5Ă reduction in proof size compared to current general-purpose ZKP systems.
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise â proof â encrypt â aggregate) is proven to be necessaryâno efficient alternative existsâand 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
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise â proof â encrypt â aggregate) is proven to be necessaryâno efficient alternative existsâand 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
Thus, the synergy of artificial intelligence (AI)-based technologies and digital financial transactions require secure anonymized methods while retaining the effectiveness of AI-based fraud-detection. This systematic review investigates stateof-the-art means of enhancing privacy assurance in ML by leveraging innovative schemes to safeguard money transfers in electronic platforms. Many privacy-preserving techniques are available and can be adopted by financial institutions to analyses encrypted data these include homomorphic encryption and federated learning. Employing these methods, AI models can identify fraudulent behavior patterns while at the same time not compromising on the privacy of single transactions. There is an extra level of security or anonymity given x by zero-knowledge proof which allows for the verification of the transactions without disclosing the data behind such transactions. Differential privacy is also used to apply noise on data to ensure that no distinguishing data set is used by the algorithm while ensuring the data is useful for statistical purposes for the ML models used. As much as its integration offers potential in carrying these privacy-shields presents some considerations. Mainly, they improve security and usersâ confidence but at the same time introduce computation cost and system intricacy. This review therefore looks at different implementation strategies and hybrid solutions which employ several ideas aimed at maintaining high efficiency of the applied privacy-preserving techniques. Security: Advanced developments in hardware acceleration and algorithms have brought into use these methods nearer to real life applications. It also explores areas of future development including quantum protection of privacy and privacy preserving AI systems. Nonetheless, time and again there are instances where researchers experienced difficulties in the actual implementation such as the approaches may not be scalable, in other words may not well work for large data sets, or that there is need to standardize these models for privacy-preserving AI to be well embraced as it remains one of the most important revolutions by which the safety of financial systems in the digital world can be enhanced. As trading volumes increase and the regulation of how clientsâ data is used gets stricter, these technologies will be at the heart of shielding consumer information whilst facilitating enhanced fight against fraud.
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.
Local Differential Privacy (LDP) enables privacy-preserving data analytics without requiring a trusted aggregator and has attracted significant attention from both academia and industry. For keyâvalue data, PrivKV has been proposed to support frequency and mean estimation under LDP. In PrivKV, the user first samples a key uniformly at random and applies a randomization mechanism to perturb the corresponding value. However, since both Sample and Perturb steps are conducted locally, PrivKV is susceptible to output poisoning attacks, where malicious users bypass these steps and submit crafted data, making the aggregation result biased. To address this vulnerability, we propose VPrivKV, a verifiable LDP protocol designed to defend against output poisoning attacks. VPrivKV enables users and the aggregator to jointly perform the sampling step using a coin-flipping protocol, while the perturbation is enforced through an interactive and verifiable mechanism. Furthermore, we propose an enhanced version of VPrivKV that integrates zero-knowledge proofs to prevent the adversary from forging the discretized value to suppress non-target keys, thereby further enhancing robustness. We theoretically analyze the privacy and robustness of the proposed protocols and conduct numerical simulations to demonstrate their effectiveness in defending against output poisoning attacks.
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 âon-chain light attest, off-chain deep store, and cross-layer verifiable bridgeâ (LPHSâXV), 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.