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.
There is a large amount of redundant data among users of cloud storage services. Client-side deduplication helps reduce the cost for service providers by avoiding repeated uploads and storage. However, this technique brings new security risks. Malicious users may use illegally obtained deduplication tags, such as file fingerprints, to fake ownership of other users’ files. Proof of Ownership (PoW) can require users to prove they have the full file, but existing methods are inefficient. They often need multiple rounds of interaction or complex computation over the whole file. As a result, the verification time increases with file size. To solve this problem, we propose a non-interactive PoW scheme based on zk-STARK. The system selects a number of challenge blocks that meet cryptographic security. It uses arithmetic circuits to encode block selection, hash computation, and the correctness of accumulators. Users only need to generate a zero-knowledge proof on these blocks. This allows them to prove they own the full file without revealing its content. The verification time does not depend on file size and appears near-constant in practice. In tests on files from 64 MB to 1 GB, our scheme is 1.2 to 46 times faster than existing methods. Security analysis shows that only a small number of blocks need to be verified. Even if an attacker knows 90% of the file, the chance of forgery is still lower than 2 − 80 . This scheme provides an efficient and practical solution for deduplication in cloud storage with strong privacy protection.
We present the Proof of Fairness Ledger (PoFL), a unified cryptographic framework for decentralized sealed-bid auctions that jointly ensures privacy, input fairness, output correctness, and public verifiability. PoFL separates a minimal Commit-Open-Verify pipeline from optional extensions, keeping the core lightweight while enabling stronger auditability when required. Its central construct, the frozen root snapshot R*, instantiated as a Merkle-style hash accumulator, immutably binds all timely commitments before the opening phase and serves as a public anchor for verification. After opening, correctness can be proven either by direct bid comparison or by a succinct Zero-Knowledge Proof of Winner, which attests inclusion, commitment consistency, and bid maximality with respect to R*. We analyze PoFL under standard cryptographic assumptions - discrete logarithm hardness, collision-resistant hashing, and the Random Oracle Model—and discuss practical efficiency for on-chain and off-chain deployment. Constant-size verification and logarithmic inclusion proofs make PoFL suitable for smart-contract implementation. Additionally, PoFL can integrate dynamic collateral management and optional force-open mechanisms to ensure liveness and economic accountability. Overall, PoFL provides a practical and theoretically grounded approach to transparent, auditable, and privacy-preserving auctions in permissionless environments.
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 key–value 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.
Cloud computing has emerged as the dominant platform for contemporary data management and service provision. However, its centralized nature poses significant risks to security, privacy, and trust. Distributed systems can enhance data integrity and auditability by incorporating blockchain technology, which offers a decentralized and tamper-resistant approach. Nevertheless, the inherent transparency of blockchain conflicts with the confidentiality requirements of cloud environments. This review paper analyzes existing studies on privacy-preserving blockchain architectures designed to secure cloud-based information systems. A systematic literature review methodology was adopted, examining forty-eight peer-reviewed studies published between 2018 and 2024. The findings reveal that researchers have explored approaches such as encryption, zero-knowledge proofs, homomorphic encryption, and hybrid on/off-chain models to balance transparency and privacy. Scalability, interoperability, and regulatory compliance remain key challenges, particularly in permissioned blockchains, which nevertheless offer advantages in governance and compliance. The study identifies research gaps and future directions, including the development of common privacy frameworks, integration of confidential computing, and establishment of standardized evaluation metrics. Overall, privacy-sensitive blockchain architectures hold strong potential for creating trustworthy and secure cloud systems.
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.
Cross-chain technology enables interoperability among otherwise isolated blockchains, supporting interactions across heterogeneous networks. Similar to how multi-hop communication became fundamental in the evolution of the Internet, the demand for multi-hop cross-chain interactions is gaining increasing attention. However, this growing demand introduces new security and privacy challenges. On the security side, multi-hop interactions depend on the availability of multiple participating nodes. If any node becomes temporarily offline during execution, the protocol may fail to complete correctly, leading to settlement failure or fund loss. On the privacy side, the need for on-chain transparency to validate intermediate states may unintentionally leak linkable information, compromising the unlinkability of user interactions. In this paper, we propose the Cross-Chain Channel Network (CCN), a decentralized network designed to support secure and privacy-preserving multi-hop cross-chain transactions. Through experimental evaluation, we identify two critical types of offline failures, referred to as active and passive offline cases, which have not been adequately addressed by existing solutions. To mitigate these issues, we introduce R-HTLC, a core protocol within CCN. R-HTLC incorporates an hourglass mechanism and a multi-path refund strategy to ensure settlement correctness even when some nodes go offline during execution. Importantly, CCN addresses not only the correctness under offline conditions but also maintains unlinkability in such adversarial settings. To overcome this, CCN leverages zero-knowledge proofs and off-chain coordination, ensuring that interaction relationships remain indistinguishable even when certain nodes are temporarily offline.
Xi Chen, Chunqiang Hu, Pengfei Hu, Xingwang Li · 5 authors
The rapid proliferation of the Internet of Things (IoT) has made resource exchange and collaboration across diverse IoT domains commonplace, necessitating secure and privacy-preserving cross-domain authentication. However, existing schemes suffer from critical limitations: they lack time-bound access control, leading to persistent unauthorized access and heightened security risks, and most are incomplete, requiring resource-intensive redeployment of cryptographic mechanisms and increasing management overhead. To address these challenges, we propose a dynamic time-bound anonymous complete cross-domain authentication scheme that leverages consortium blockchain for decentralized trust, embeds dual temporal constraints, expiration time and permissible authentication periods, into credentials for fine-grained access control and automatic natural revocation, and employs accumulators and non-interactive zero-knowledge proofs (NIZKs) to enable anonymous authentication while ensuring strong privacy protection. Crucially, the proposed scheme achieves complete cross-domain authentication without modifying existing cryptographic mechanisms, significantly reducing overhead in computational, communication, and storage. Security and performance analyses confirm that the proposed scheme not only guarantees robust security and privacy but also outperforms existing schemes in efficiency.
An important cryptographic mechanism that guarantees confidentiality (the zero-disclosure property) and ensures that it is impossible to prove a false statement to the verifier is zero-disclosure proofs. A popular implementation of zero-disclosure proofs is short, noninteractive proofs that can be quickly verified and that do not require interaction between the parties after the initial setup. The main direction in the development of modern proof systems is interactive proof, which is built in two steps. The first is sending a confirmation of the polynomial of an interactive oracle proof and the second is creating correct oracles of the polynomial commitment scheme using well-defined cryptographic methods for evaluating polynomials. Verifying the use of the same coefficients in each linear combination requires checking both polynomial consistency and variable consistency. To construct general schemes of concise non-interactive zerodisclosure knowledge argument, an interactive oracle proof polynomial was proposed that models messages as polynomial oracles. All tests are proved using polynomial commitment schemes and then evaluated with zero knowledge at a point specified by the person verifying the information. The reliability and confidentiality of all tests are based on three main categories of interactive oracle proof polynomials, namely polynomial commitment schemes with conjunction, with inner product argument and with code theory. The protocols of concise noninteractive zero-disclosure knowledge arguments are implemented through high-level programs (compilers), which are converted into an intermediate representation, i.e. a scheme defined by a system of constraints. The compilers used are divided into domain-oriented languages, embedded domain-oriented languages, and zero-knowledge virtual machines. Specialized domain-oriented hardware description languages or programming languages offer an adapted syntax for efficiently expressing constraints in arithmetic schemes. Embedded domain-oriented languages are implemented as functions in general-purpose programming languages and are oriented to the overhead schemes inherited from the embedded language. Zero-knowledge virtual machines process the opcode of the fetch-decodeexecute cycle, replicating the computation trace for general programs and generating corresponding zeroknowledge proofs. They are compatible with existing high-level programming languages and can use the features of existing compilers. Compilers are evaluated for cross- or syntactic compatibility. In general, the biggest obstacle to using non-interactive proof libraries is the lack of documentation. Standardization can help developers compare important features across libraries and establish a more consistent performance baseline. Library documentation for these core features is implicit, and developers need to understand the underlying cryptographic techniques to choose an appropriate scheme. Standardization of compiler options is important, making it difficult to reuse existing tools.
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.
In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.
This paper challenges the prevailing assumption in Central Bank Digital Currency (CBDC) design that comprehensive transaction surveillance is necessary for financial stability and crime prevention. We propose an alternative privacy-preserving architecture that achieves equivalent or superior fraud detection through mechanism design rather than identity monitoring. Key contributions: Separation of pattern detection from identity: Transaction graph analysis identifies structural anomalies without accessing participant identities Transaction-level intervention: Suspicious activity flags individual transactions, not accounts or users Opt-in deanonymization: Identity revelation is always voluntary; users may abandon flagged transactions without consequence Architectural enforcement: Privacy guarantees are structural, not policy-dependent The framework inverts the burden of proof in financial surveillance. Rather than requiring users to demonstrate legitimacy, it requires the system to demonstrate suspicion—and even then, users retain the option to walk away. This creates a game-theoretic deterrent where illicit actors cannot complete transactions, while legitimate users experience minimal friction. We demonstrate that privacy-preserving CBDC architecture is technically feasible using established cryptographic primitives (zero-knowledge proofs, secure multi-party computation, threshold cryptography) and that the choice to implement surveillance infrastructure represents a policy decision rather than technical necessity. Part of the Adversarial Systems Research program investigating friction dynamics in complex systems where competing interests generate structural conflict.
The growth of cloud computing in the healthcare field has led to significant developments, but ensuring the confidentiality and protection of medical records such as electronic health records (EHRs) remains a major concern for healthcare service applications. In cloud computing, the basic authentication provided by most service providers is insufficient to ensure secure access to critical or sensitive resources. Moreover, most of the existing healthcare management systems are ineffective in handling a number of patient data, which leads to single points of failure. To address these issues, elliptic curve cryptography (ECC) with Curve25519 is utilized to enhance security in cloud storage, particularly within healthcare management systems. The ECC with Curve25519 is optimized for efficient and fast scalar multiplication, which reduces computational overhead and enhances performance. The curve parameters are selected to prevent vulnerabilities and ensure security against known attacks. Moreover, it is efficient in maintaining the integrity of patient records, which reduces storage and bandwidth requirements. The ECC with Curve25519 achieves lower Key-Gen, prove, verify, proving key size, and verification key size of 13.7 s, 48 s, 0.608 s, 13.27 Mb, and 123.70 Kb, respectively, in comparison with proxy re-encryption algorithm with zero-knowledge proof (ZKP).
Chi Zhang, Fenhua Bai, Xiaohui Zhang, Jinhua Wan · 6 authors
As a middleware technology in distributed computer systems, blockchain systems represent a paradigm for achieving node interconnectivity. Despite this, technical differences between various blockchain networks have led to the emergence of a phenomenon known as multi-chain, where inter-chain communication has become a trust barrier. Cross-chain technology is a powerful tool that allows data to flow between different blockchain networks, breaking down data barriers and enabling seamless data transfer. However, cross-chain identification may lead to potential risks such as the exposure of private information and data loss or tampering. In this brief, we propose Universal Cross-Chain Permissioned Blockchain (UCCPB) architecture, which connects single permissioned chains into a multi-chain system. Based on this, the Cross-Chain Anonymous Identity Authentication (CCAIA) model is proposed, which implements privacy-preserving chain identity registration and verification through zero-knowledge proof without a trusted setup. Furthermore, we propose the Proof of Cross-Chain Invocation (PoCI) mechanism of UCCPB, which consists of a node election and consensus on the invocation result. This mechanism ensures the correctness of the cross-chain invocation results and incentivizes nodes to participate in UCCPB. Our experiments show that the proposed UCCPB achieves a balance between performance and privacy while improving the security of cross-chain invocations.
Sana Ullah, Syed Muslim Jameel, Meghann Drury-Grogan, Mara Sintejdeanu · 5 authors
The complexity of cross-border regulatory compliance in the MedTech sector imposes significant administrative and financial burdens on manufacturers, characterized by manual processes, data redundancy, and country-specific, cross-border heterogeneous regulations. To address this, we present EireLedger, a decentralized framework that automates and cryptographically enforces regulatory compliance verification. EireLedger utilizes a novel dual-purpose zero-knowledge proof (ZKP) scheme, instantiated with Groth16 zk-SNARKs, which allows a manufacturer to prove a device dossier's compliance to a jurisdiction-specific regulator in a privacy-preserving manner, while simultaneously generating a verifiable ZKP-based access grant for the regulator. This cryptographic proof is immutably anchored to a permissioned Hyperledger Fabric blockchain, which orchestrates the protocol and maintains a minimal, auditable record. The corresponding encrypted dossier artefacts are stored off-chain in a private IPFS cluster. Our comprehensive evaluation demonstrates that on-chain proof verification is highly efficient with a median latency of 12.3 ms, and our integrated ZKP-as-access-control model reduces end-to-end audit latency by 40% compared to traditional attribute-based access control (ABAC) by eliminating external authorization calls. The on-chain storage footprint is constant at ~2.1 KB per audit, ensuring data minimization. The framework also supports right to erasure in compliance with GDPR, cryptographically unpinning a 5 GB dossier in under 90 s. These results establish EireLedger as a novel, privacy-preserving, and practical solution for cross-border regulatory compliance in the MedTech supply chains.
Blockchain Technology Applications and Security
Big Data and Digital Economy
Physical Unclonable Functions (PUFs) and Hardware Security
Academic credential fraud and falsification of research outputs remain persistent challenges in higher education and research communities. Traditional centralized credential verification systems are vulnerable to tampering, slow verification processes, and lack of transparency. This research introduces a robust blockchain-based consortium framework for transparent and tamper-proof verification of academic credentials and research outputs. Unlike prior works that primarily address identity or degree validation, our system integrates universities, accreditation authorities, and publishers into a multi-layered consortium blockchain, ensuring trust among multiple stakeholders. To preserve privacy, zero-knowledge proofs (ZKPs) are applied, enabling credential verification without disclosing sensitive personal data. The framework also introduces a dynamic revocation mechanism to handle fraudulent, plagiarized, or revoked certificates and publications. A prototype implementation on Hyperledger Fabric demonstrates feasibility, achieving high throughput (182 TPS), low latency (1.2 s average block confirmation), and efficient scalability with multiple nodes. Our results highlight the potential of blockchain in building a global, tamper-proof, privacy-preserving academic verification ecosystem, addressing credential fraud and ensuring research integrity.
This chapter explores the extent of the complex relationship between cryptocurrency and unexplained wealth, emphasizing the dual nature of these digital assets as both tools for legitimate wealth accumulation and facilitators of financial crime. Despite their potential for investment and trading, cryptocurrencies have been implicated in significant criminal activities, with estimates indicating that nearly 46% of Bitcoin transactions involve illicit behaviour. This analysis delves into the mechanisms by which cryptocurrencies allow for wealth accumulation – ranging from investing in volatile markets to participating in initial coin offerings – while examining the inherent risks of anonymity, decentralisation, and minimal regulatory oversight that attract criminal actors. Furthermore, the paper discusses how these features enable market manipulation and facilitate illegal trade on dark web platforms. Additionally, this chapter discusses how cryptocurrencies enable wealth accumulation and offer mechanisms for obfuscating wealth, mainly through privacy coins like Monero and Zcash. These coins incorporate advanced encryption techniques, such as ring signatures, stealth addresses, and zero-knowledge proofs, which complicate efforts by law enforcement to trace transactions and identify users. Ring signatures obscure the identity of transaction signers, while stealth addresses disconnect the sender and receiver, ensuring anonymity. Zero-knowledge proofs provide verification without revealing sensitive information, enhancing confidentiality further. Finally, tumbling and mixing services aggregate multiple users’ transactions, obscuring the financial trail and making asset recovery exceedingly difficult.
Abstract A hybrid lattice-based commitment scheme is proposed for anonymous proofs between hidden values. The method is based on a modification of the BDLOP zero-knowledge proof (ZKP) scheme by replacing the learning with errors (LWE) problem with a learning with rounding (LWR) problem, which theoretically makes it possible to reduce the size of the parameters and reduce the complexity of parameter selection. It is shown that the proposed scheme preserves the property of additive homomorphism, which makes it possible to apply it to prove linear relations. The obtained results can be applied to construct electronic voting protocols or conduct anonymous transactions.
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.
Applies the Evidence-Based Subjective Logic (EBSL) framework to zero-knowledge reputation systems and decentralised identity. Demonstrates how reputation opinions that are provably correct can be published without revealing the underlying evidence graph, using the EZKL zkML framework for proof generation.
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.
A proof-carrying trust framework where every EQBSL trust claim ships with a zero-knowledge validity certificate verifiable by any third party without re-running the computation. Establishes the formal link between evidence-based subjective logic opinions and zero-knowledge proof systems, enabling trustless trust attestation in decentralised networks.