This study focuses on the core needs of consumer data privacy protection in the context of the digital economy and creates a blockchain-based privacy and security architecture. Through a layered design, this architecture effectively combines data collection, blockchain core, privacy computing, smart contracts, and application integration modules. It integrates key techniques such as zero-knowledge proofs, homomorphic encryption, and decentralized identity to ensure that data is encrypted and stored throughout its creation and destruction, implements meticulous access rights management, and implements a verifiable audit process. The dataset used in this experiment is the 2024 CMS market county-level administrative district public dataset in the United States. In an environment simulating actual business pressures, the privacy protection effectiveness, system scalability, and computational and storage costs of this proposed system are tested. Comparisons are made with two typical implementations. While ensuring differential privacy and k-anonymity, the proposed system improves data transmission speed, reduces processing latency, and reduces storage consumption. This demonstrates the potential and superior performance of this system across multiple entities and industries. This study provides a practical and feasible technical implementation for blockchain-driven consumer data privacy protection and offers a verifiable engineering reference for data governance and cross-industry data sharing in the United States.
To harness the commercial potential of medical data, various blockchain-based data-sharing and exchange platforms have been proposed. A key challenge is accurately tracing the provenance and transformations of medical data assets throughout the exchange process. Existing methods cannot facilitate exchanges of publicly hosted datasets while maintaining fairness, as they require revealing keys to the blockchain during interactions. This paper presents MEDET, a novel data exchange scheme that ensures traceability of medical data assets while protecting data privacy and guaranteeing exchange fairness. MEDET leverages zero-knowledge proofs to securely verify transformations within medical datasets and confirm data authenticity. Unlike previous schemes, MEDET supports both simple data exchanges and detailed tracking of data transformations and transaction histories, aiding in the provenance and value assessment of medical records. Additionally, MEDET features a key-secure protocol for fair exchange without disclosing symmetric keys. Compared to existing fair exchange schemes, MEDET uniquely ensures the privacy of publicly hosted data while simultaneously upholding the exchange fairness. The security analysis of MEDET demonstrates its security and privacy properties. The evaluation of MEDET indicates that it outperforms existing schemes in tracking data transformations and facilitating exchanges.
As privacy concerns intensify in data-driven systems, this article presentsPriMa$\chi $, a hybrid framework that combines a genetic algorithm (GA) and reinforcement learning (RL) to optimize the privacy–utility tradeoff in differential privacy (DP) through explicit adaptive privacy–utility control. PriMa$\chi $adaptively selects perturbation configurations to minimize the privacy budget$(\varepsilon)$while preserving data utility$({\mathcal {U}})$. To support verifiable privacy-preserving analytics in decentralized environments, we further integrate PriMa$\chi $with a privacy-aware smart-contract framework that enables on-chain DP enforcement and zero-knowledge proof (ZKP) verification. The framework supports structured, transactional, and spatiotemporal workloads, including decentralized finance, electronic health records, census analytics, and location services. An interleaved Petri net model is used to formally verify privacy-aware state transitions in the smart-contract workflow. Experimental results show that PriMa$\chi $achieves utility of at least 80% under dataset-dependent privacy budgets in the range$0.003 \leq \varepsilon~\leq\unicode{0x0142}.43$, while also effectively mitigating model-extraction, membership-inference, and privacy-budget-exhaustion attacks. These results demonstrate that PriMa$\chi $provides adaptive, auditable, and practically deployable privacy protection for decentralized analytics.
Federated Learning (FL) enables collaborative model training across decentralized clients while preserving data privacy. However, practical deployments are often limited by significant communication overhead and vulnerability to Byzantine poisoning attacks. Existing defenses typically rely on post-hoc anomaly detection, but executing complex distance metrics or clustering on encrypted, sparsified updates creates a substantial computational burden for the aggregation server. We present a privacy-preserving FL framework that addresses these challenges by integrating Top-ksparsification, non-interactive zero-knowledge proofs (NIZKPs), and homomorphic encryption. Instead of relying on expensive ciphertext distance computations, our architecture uses a pre-aggregation global mask sign vector, generated through majority voting, to filter anomalous updates. This mechanism treats unselected gradient coordinates as explicit zero-votes, which mitigates malicious coalitions attempting to manipulate disjoint parameter subsets. A local error feedback mechanism ensures that heterogeneous client updates align over successive training rounds. Combined with NIZKPs to enforce coordinate-wise magnitude bounds, the framework provides Byzantine resilience without increasing communication costs or compromising privacy. Evaluations on MNIST and CIFAR-10 show that our approach maintains high communication efficiency and robustness. Under a 40% malicious client poisoning attack and a 50% sparsification ratio, the framework achieves final accuracies of 92.14% and 63.20%, respectively, demonstrating its effectiveness in bandwidth-constrained, hostile environments.
Federated learning across IoT devices must simultaneously protect each device’s update from disclosure, prevent malicious participants from biasing the global model, and hide which devices are participating from outside observers. Existing systems typically address only a subset of these goals: secure aggregation hides individual updates but cannot validate them, plaintext-based robust filtering requires the server to see updates, and most cryptographic pipelines ignore timing privacy. This paper presents TriSAFE, a protocol composition for IoT federated learning with a single coordinating server and three threshold helpers. The server holds no decryption key. TriSAFE combines four mechanisms that are usually studied in isolation: (i) encrypted client updates accompanied by zero-knowledge proofs that each coordinate lies within a bounded range; (ii) a new lightweight binding step (the plaintext-equivalence protocol, PEP) that cryptographically ties the values proven in zero knowledge to the exact ciphertext later aggregated by the server, closing a substitution gap left by range proofs alone; (iii) helper-added differential privacy noise applied homomorphically before any decryption, so the server only ever sees a noised aggregate; and (iv) fixed-cadence batching with calibrated cover traffic to hide participation from passive network observers. Across two IoT intrusion-detection benchmarks (Edge-IIoTset and N-BaIoT) and MNIST, TriSAFE keeps accuracy within 0.1-2.1 percentage points of the no-attack baseline under Byzantine, label-flip, FANG, and time-delay attacks, with attack success rate below 1% (<0.1% for FANG). Timing inference by a passive observer drops close to chance, and the end to end overhead is 7-36% relative to a non-defended baseline. On MNIST, TriSAFE achieves 89-91% accuracy, 15-17 points above the MODEL benchmark under the same attack suite. The design is practical for gateway-assisted IoT deployments under the assumption that the coordinator does not collude with two helpers and that at least two helpers contribute honest DP noise.
Decentralized finance (DeFi) platforms have experienced a rapid increase in fraudulent activities such as price manipulation, wash trading, and anomalous transaction behavior, while traditional centralized fraud detection methods remain unsuitable due to privacy and regulatory constraints. This study proposes a privacy-preserving fraud detection framework using federated learning, enabling multiple decentralized entities to collaboratively train a machine learning model without sharing raw transaction data. A real-world decentralized exchange (DEX) dataset containing over 100,000 transactions is preprocessed and enhanced through feature engineering techniques capturing swap rate deviations, transaction volume anomalies, and temporal patterns. In the absence of labeled fraud data, a heuristic-based labeling approach is employed to simulate realistic fraud scenarios. A Logistic Regression model is trained across multiple distributed client nodes, with model parameters aggregated using the Federated Averaging (FedAvg) algorithm over several communication rounds. The experimental findings show that the federated model delivers results similar to centralized methods while preserving data privacy, proving it to be an efficient solution for secure and scalable fraud detection in decentralized financial environments.
The secure, privacy-preserving sharing of genomic data across multiple institutions is a critical enabler for precision medicine, yet it remains fundamentally constrained by the identifiability and immutability of genomic data. While blockchain technologies have been proposed to provide decentralized governance, auditability, and tamper resistance for genomic data sharing, blockchain-only solutions are insufficient because they expose transaction metadata, access patterns, and smart-contract logic, leaving significant privacy risks unresolved. Zero-Knowledge Proofs (ZKPs) have recently emerged as a key cryptographic primitive for addressing such limitations, enabling verifiable access control, policy compliance, and computation correctness without disclosing sensitive genomic data. Although several surveys examine ZKPs or blockchain in isolation or across heterogeneous application domains, there is currently no dedicated survey that systematically analyzes their combined use in multi-party blockchain-based genomic data sharing systems. This paper addresses this gap by presenting a comprehensive, domain-specific survey of ZKP-enabled blockchain architectures for genomic data sharing. We classify existing approaches by architectural models, ZKP techniques, governance mechanisms, and threat-mitigation capabilities, and then compare their assumptions, performance characteristics, and deployment maturity. Furthermore, we identify open challenges in scalability, interoperability, proof overhead, and regulatory compliance, and outline future research directions for secure, scalable, and ethically compliant genomic data-sharing ecosystems.
Takmer všetky systémy pre správu identít, centralizované alebo decentralizované, používajú na zaistenie Sybil-rezistencie, ochrany pred útokmi, ktoré využívajú veľké množstvo falošných identít, centralizovaný prístup. Tieto systémy zvyčajne vyžadujú overenie prostredníctvom telefónneho čísla alebo dokladu totožnosti vydaného štátom. Cieľom tejto práce je navrhnúť a implementovať decentralizovaný systém pre správu identít, ktorý zabezpečí Sybil-rezistenciu, anonymitu používateľov a súkromie ich osobných údajov. Navrhované riešenie využíva overenie biometrie tváre a anti-Sybil analýzu sociálneho grafu. Pre zabezpečenie decentralizácie, anonymity a ochrany súkromia sa využívajú technológia blockchain, zero-knowledge proofs a trusted execution environments. Výsledkom je robustný systém, ktorý zabezpečuje jedinečnosť registrovaných užívateľov, ich anonymitu a súkromie bez spoliehania sa na centralizované autority. To predstavuje významný krok k dosiahnutiu samostatnej správy identít, ktorá je v dnešnom svete kľúčová, keďže osobné údaje sú cennou komoditou.
We present CHRONOS, the first autonomous AI agent that simultaneously achieves plaintextblindness (all data is processed under fully homomorphic encryption without ever beingexposed), cryptographically enforced time bound existence (the agent’s own decryption key islocked behind a publicly verifiable proof of sequential work, rendering it inaccessible until aprecise future moment), and remote verifiability of self destruction (a zero knowledge proofcertifies that the key material has been irreversibly destroyed after mission completion). Theagent’s operational lifespan is governed by a “cryptographic fuse” constructed from a proof ofsequential work (PoSW) whose computation time accurately matches the intended missionduration. A drand decentralized randomness beacon serves as a trusted time oracle to trigger thefinal key shredding. Crucially, the erasure proof is a non interactive zero knowledge argument(SNARK) that proves the correct execution of the entire self destruction sequence—including thePoSW solution, decryption of the private key, and subsequent memory zeroization—enablingany third party to cryptographically verify the agent’s annihilation without trusting the agent orits hardware. We provide a complete system architecture, a formal security model with gamebased definitions and reductions to standard assumptions, and a proof of concept implementationusing Zama’s TFHE rs for encrypted inference, a Cohen Pietrzak PoSW implementation, and aGroth16 SNARK. Our benchmarks indicate that FHE inference on a small neural network (50 Kparameters) completes in seconds, the PoSW background thread consumes negligible resources,and the erasure proof can be generated and verified in under three seconds. CHRONOSrepresents a fundamental advance in secure, disposable AI agents, with immediate applications indefense, intelligence, and high privacy environments.
Digitale Identitätssysteme bilden eine zentrale Grundlage moderner Verwaltungs- und E-Government-Prozesse. Sie ermöglichen die sichere Interaktion zwischen Bürger:innen, staatlichen Stellen und privaten Diensten. Ein besonders kritischer Schritt ist dabei die Identitätsprüfung im Rahmen des Onboardings, da hier die Verbindung zwischen einer realen Person und einer digitalen Identität hergestellt wird. Bestehende Onboarding-Verfahren, etwa persönliche Identifikation, VideoIdent, biometrische Verfahren oder dokumentenbasierte Prüfungen, stellen dafür etablierte Mechanismen bereit, erfordern jedoch häufig die Verarbeitung sensibler personenbezogener Daten und stützen sich stark auf organisatorische Vertrauensstrukturen.Die vorliegende Arbeit untersucht, ob ein deterministischer, registerbasierter und kryptographisch unterstützter Matching-Ansatz auf den Onboarding-Prozess von ID Austria angewendet werden kann. Der Fokus liegt dabei nicht auf der Entwicklung eines neuen kryptographischen Bausteins oder eines vollständig neuen Identitätssystems, sondern auf der konzeptionellen Anwendung und prototypischen Umsetzung von Deterministic Privacy-Preserving Identity Matching als Onboarding-Modell. Dieser Ansatz wird im Rahmen der Arbeit als DPPIM-OM bezeichnet.Die Arbeit folgt einem konstruktiv-analytischen Vorgehen. Zunächst werden die technischen und konzeptionellen Grundlagen digitaler Identität, Identitätsprüfung, privacy-preserving Matching, OPRF/VOPRF-Mechanismen und Zero-Knowledge-Nachweisen analysiert. Darauf aufbauend wird ein Onboarding-Modell beschrieben, das deterministischen Full-Match, kanonisierte Attributrepräsentation, servergebundene kryptographische Auswertung, registerbasierten Vergleich und registergebundene Nachweisführung kombiniert. Anschließend wird ein Prototyp umgesetzt, um die technische Realisierbarkeit des Ansatzes unter kontrollierten Bedingungen zu demonstrieren.Das vorgeschlagene Onboarding-Modell wird dem aktuellen ID-Austria-Onboarding sowie VideoIdent-, biometrischen und dokumentenbasierten Verfahren gegenübergestellt. Die Evaluierung erfolgt entlang zentraler Dimensionen wie Datenexposition, Informationsleckage, Sicherheit, Missbrauchsresistenz, Vertrauensmodell, Verifizierbarkeit, Determinismus, Fehleranfälligkeit, Anforderungen an Datenqualität, Prozesskomplexität, Performance sowie Kompatibilität mit dem europäischen regulatorischen Rahmen.Die Ergebnisse zeigen, dass DPPIM-OM insbesondere in den Bereichen Datenminimierung, Informationskontrolle und Verifizierbarkeit deutliche strukturelle Vorteile aufweist. Gleichzeitig bringt der Ansatz spezifische Anforderungen und Einschränkungen mit sich, insbesondere hinsichtlich Datenkonsistenz, technischer Umsetzungskomplexität und fehlender direkter Personenbindung. Die Arbeit kommt zu dem Ergebnis, dass der Ansatz eine vielversprechende Möglichkeit zur Weiterentwicklung digitaler Onboarding-Prozesse darstellt, insbesondere in hybriden Modellen, die klassische Mechanismen zur Personenbindung mit einem deterministischen und kryptographisch überprüfbaren Attributabgleich kombinieren.
Zero-knowledge proof is a special cryptographic technique that allows a prover to convince a verifier about the correctness of a claim without explicitly disclosing the claim itself. With the advancements of blockchain technologies, zero-knowledge proof has been successfully integrated into many decentralized applications over the years. ZoKrates, with its ease-of-use and direct integration to blockchain platforms, has emerged as a leading framework for developing, generating and verifying zero-knowledge proofs. This survey compiles a corpus of 347 documents that cite the original research work of ZoKrates by considering the period ranging from 2018 to 2025. Out of this corpus, this survey systematically selects and analyzes a total of 87 different documents including only peer-reviewed publications and excluding the gray literature. To the best of our knowledge, this is the first survey in the literature to follow a systematic approach to analyze the privacy- preserving applications in blockchain from the perspective of ZoKrates. This survey presents three different classifications over the documents with respect to (i) the applications they develop, (ii) the challenges they frequently encounter and (iii) the metrics they often use to measure performance of their techniques. Based on the challenges identified, this survey finally discusses numerous future research directions to promote potential advancements in the field and attract the attention of scientific and industrial communities. Feedback from readers regarding any inaccuracies or misinformation in this survey is welcome.
Federated Learning (FL) enables collaborative training without centralizing sensitive data but faces challenges, including client authenticity, verifiable training participation, and secure aggregation. To overcome these challenges, we propose a novel framework, Zero-Knowledge Reputation-aware Blockchain Federated Learning (ZK-RBFL), which integrates blockchain, FL, Homomorphic Encryption (HE), and zero-knowledge proofs (ZKP). In the proposed ZK-RBFL framework, initially the clients undergo lightweight token-based authentication and then generate ZKP to provide cryptographic evidence of honest local training participation and reported inference accuracy before contributing their model updates. The model updates are encrypted using the CKKS HE mechanism to prevent any potential model inversion attacks. These encrypted model updates are stored on IPFS, with their corresponding CIDs recorded on the blockchain to ensure immutability. Further, ZK-RBFL enables mutual client verification of ZKPs to reduce server bottlenecks and enhance accountability. To ensure fairness and robustness in a distributed environment, we introduce a democratic blockchain consensus mechanism named Proof of Reputation-Weighted Voting (PoRWV) for block acceptance. Once consensus is reached, the encrypted model updates are aggregated using reputation-weighted averaging. We demonstrate the effectiveness of ZK-RBFL for brain tumor classification using a ZKP-compatible LeNet model for proof generation. Despite model simplicity, the global model achieves 94.22% accuracy. In addition, experiments with malicious clients and formal Scyther security analysis demonstrate that ZK-RBFL ensures both security and performance.