Quasi-adaptive non-interactive zero-knowledge (QA-NIZK) arguments are fundamental cryptographic primitives widely used in privacy-preserving technologies such as anonymous credentials, group signatures, e-cash, and blockchain-based applications. We present the first tightly secure unbounded simulation sound quasi-adaptive non-interactive zero-knowledge argument system from simple assumptions. The construction has a security loss ofO(1), a compact common reference string, constant size proofs, and its security relies on the hardness of the well-known SXDH assumption. Our result improves state-of-the-art (Couteau and Hartmann, CRYPTO 2020) in terms of the proof size (about three times), a lower security loss, and also with respect to the underlying hardness assumptions. The tight security reduction enables shorter key-length recommendations, leading to improved concrete efficiency. Our main technical contribution is a novel proof technique inspired by the randomization technique of the Naor-Yung double-encryption paradigm and the adaptive partitioning due to Hofheinz (EUROCRYPT 2017).
Rajesh Bose, Shrabani Sutradhar, Arfat Ahmad Khan, Sandip Roy · 7 authors
ABSTRACT The promise of blockchain applications is transformative in terms of certificate verification and managing digital identities in the various fields, such as education, healthcare and land records. Nevertheless, current blockchain‐based certificate solutions have serious shortcomings: most are based on simple cryptography protection with no privacy‐preserving systems, have low throughput (16.67 TPS in typical Ethereum‐based systems), have unpredictable response times under varying loads, are not standardised across industries and are expensive to operate due to gas fees. Besides, the current implementations are mostly either theoretical or without performance tests in practice. This paper fills these gaps by suggesting a Plonk‐based system that incorporates zero‐knowledge proofs, digital signatures and trusted identity verification to improve the efficiency, security, and privacy of the verifiable credential digital identity verification and management systems (VC DIVMS). It was implemented at JIS University, India, with 50 transactions per minute (an improvement of 200% over Ethereum), an error rate of 244–1277ms in response times under load conditions (24‐33 faster than Ethereum) and high‐level privacy through ZKP. Contrary to currently used models that are sector‐specific, the offered Plonk framework offers a single, scalable, privacy‐focused model that can be applied in areas of education, healthcare, or credit verification. Intense testing ensured both resilience, scalability and compatibility with a demanding environment, making Plonk a strong and secure substitute to decentralised identity verification and credential management that is resistant to tampering.
This thesis advances privacy-preserving solutions essential for addressing contemporary technological challenges in smart cities, decentralized systems, and algorithmic decision-making processes. Firstly, we introduce a hybrid sensing framework integrating Internet of Things (IoT) sensors and crowdsensing techniques to overcome limitations inherent in traditional methods. The hybrid sensing model incentivizes voluntary user contributions to complement fixed-location IoT sensors, ensuring reliable and comprehensive data collection while maintaining user anonymity through a privacy-preserving protocol. We implement this model in a smart parking application, demonstrating significant improvements in data accuracy and user engagement. Secondly, we propose a decentralized anonymous crowdsourcing system leveraging blockchain technology, which removes reliance on centralized intermediaries, thereby enhancing transparency and mitigating biases. Our system integrates anonymous payments using the Zerocoin protocol framework, eliminating the need for worker identity registration and trusted setups, thus fostering genuinely anonymous participation. Empirical analyses confirm that our approach maintains practical efficiency in transaction verification and moderate blockchain gas costs. Lastly, we tackle fairness and transparency in algorithmic decision-making processes, addressing public concerns regarding inherent biases and opaque computational practices. We develop a privacy-preserving, publicly verifiable framework that combines succinct zero-knowledge proofs with blockchain infrastructure, allowing independent verification of algorithmic fairness without exposing sensitive inputs or decision-making algorithms. Our concrete instantiation employs a restricted KZG polynomial commitment scheme alongside the Sonic zk-SNARK protocol, demonstrating small proof sizes, efficient verification, and practical deployment feasibility. Collectively, this thesis contributes significantly to the field by providing robust, scalable, and privacy-conscious technologies tailored for contemporary smart city applications and decentralized computational ecosystems.
Mohammad Rafiqul Islam, Silicon-Saffat TRISDUCTION
The P versus NP problem, formalized by Cook (1971) and designated a Clay Millennium Prize Problem in 2000, asks whether every computational problem whose solution can be verified in polynomial time can also be solved in polynomial time. For fifty-five years the problem has resisted all single-axis formal resolution attempts. Three independently proven barrier results have demonstrated that all currently known classes of mathematical proof technique are structurally incapable of settling the question within the formal axis alone. This paper presents a unified geometric determination of both P = NP and P ≠ NP using the Trisduction ENGINE, an epistemic certification architecture operating across three orthogonal warrant-vectors: Formal (V_F), Empirical (V_E), and Phenomenological (V_P). Version 10.0 introduces two architectural upgrades over prior versions: Rule 9 Axiomatic Quarantine, which formally removes the Turing Machine abstraction from the framework's admissible baseline and replaces it with the Tri-Layer Plenum topology; and the Meta-Epistemic Hierarchy (Geometry > Mathematics > Logic), which resolves the recurring drift pattern in which formal demands were treated as epistemically superior to geometric physical measurement. The two audits are presented as a single master document to make the asymmetry between the claims structurally transparent: P = NP carries zero positive warrant across all three axes and is stopped at Gate 2; P ≠ NP passes all twelve gates with three fully independent, orthogonal warrant-vectors. The determination is explicitly non-deductive. It does not constitute a traditional mathematical proof and does not satisfy the Clay Mathematics Institute's criteria. GOL [⟀] is defined as the strongest achievable non-deductive epistemic warrant: the geometric fact that three orthogonal planes exhaust all degrees of freedom in the epistemic space, leaving no room for the alternative claim to occupy. The paper's central phenomenological contribution is the dual anchoring of V_P through the Zero-Knowledge Proof conviction gap and the Frame-Independent Observer actualization boundary. Both sources survive the Linguistic Isolation Test against V_F and V_E vocabulary, the Deletion Test, and four rounds of post-certification stress-testing documented in the appendices. The Convergence Dissolution Test finds irreducible residue in all three vectors under the strongest single-factor account. The Living Verifiable Proof — the Engine's simultaneous perfect verification capacity and structurally total generative incapacity at the Isometric Plenum boundary — provides continuously falsifiable phenomenological evidence that checking does not entail finding.
With the rapid increase of terminal devices in the Internet of Things (IoT), it has become a significant challenge to achieve real-time and privacy-preserving data aggregation. To address this challenge, edge computing has emerged as an effective paradigm to reduce latency, where a privacy-preserving data aggregation scheme is exploited to preserve data privacy. However, most existing privacy-preserving data aggregation schemes are limited by fixed data dimensions, low scalability, and high communication or computational overhead. To address these shortcomings, this paper proposes a multidimensional privacy-preserving data aggregation scheme that supports flexible dimension expansion and privacy protection in edge computing systems. The scheme integrates the Chinese Remainder Theorem (CRT) with an elastic modulus set to efficiently pack multidimensional data. This design enables terminal devices to add new data dimensions without interrupting current operations or modifying historical data. Furthermore, by exploiting Bulletproofs-based zero-knowledge proofs and Bellare-Neven (BN) signatures with half-aggregation, the proposed scheme enables lightweight and scalable batch verification of data integrity and authenticity. These mechanisms effectively reduce the verification workload and communication bandwidth in large-scale deployments. In addition, an optimized Paillier homomorphic encryption algorithm is used to enable efficient aggregation of encrypted multidimensional data. Experimental results and theoretical analysis show that the proposed scheme significantly reduces computational and communication costs compared with existing methods.
Blockchain technology, in simple words, is an innovative force that democratizes the methodologies of financial transactions by creating safe, traceable, and unalterable digital data. This research investigates how blockchain increases financial transparency in banking, government, and supply chain management for different sectors. It identifies block chain’s core features: decentralized ledgers, real-time auditing, and transparent data sharing, in total reducing information asymmetry and thus fraud, increasing public trust. This study will focus on the role of block chain in financial reporting, as immutable transaction records ensure audit-free error-free error checks and compliance with regulatory standards. The primary use cases for this are anticorruption government procurement systems, banking networks improving fraud detection, and supply chain platforms ensuring product traceability. Smart contracts integrated into financial processes help reduce intermediaries and promote accountability. The paper concludes with an overview of emerging trends in zero-knowledge proofs, decentralized finance, and blockchain-based governance systems that may transform the standards of transparency. Some policy recommendations for leaders are investment in blockchain research, the development of regulatory frameworks, and fostering cross-industry collaboration. Blockchain technology is expected to redefine financial transparency through accountability, fraud reduction, and increased public trust in digital economies.
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.
Yan Watequlis Syaifudin, Vipkas Al Hadid Firdaus, Imam Fahrur Rozi, Chandrasena Setiadi · 8 authors
The digitization of health records has enhanced clinical efficiency, but amplified risks related to data privacy, integrity, and auditability.While permissioned blockchains offer immutability and traceability, they often fail to reconcile transparency with confidentiality-either exposing sensitive data or obscuring it beyond regulatory scrutiny.To address this gap, this paper presents an integrated framework that combines Zero-Knowledge Proofs (ZKPs) with a permissioned blockchain to enable verifiable yet private healthcare transactions.A visit centric Electronic Health Record (EHR) model supports three real-world use cases: medication validity, procedure confirmation, and demographic verification.A four-layer architecture decouples data, application logic, cryptographic trust, and audit logging, allowing end-to-end validation without raw data disclosure.Experimental evaluation across three ZKP libraries (snarkJS, ZoKrates, and gnark) on a synthetic dataset of 1,000 patient visits demonstrates sub-500 ms verification latency, with snarkJS selected for its ecosystem compatibility despite slower raw performance.End-to-end pipeline latency averages 1.35 s, confirming feasibility for batch workflows such as insurance claims.The system further includes a web-based auditor interface that validates tamper-evidence under off-chain attacks, bridging cryptographic guarantees with operational compliance.
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.
The rapid development of the Internet of Medical Things (IoMT) has also facilitated real-time monitoring of healthcare, yet creates major issues of security, privacy, and interoperability, particularly in terms of emerging threats of quantum computing. This paper introduces a quantum-resilient blockchain paradigm, which combines post-quantum cryptography (PQC), Zero Knowledge Proofs (ZKPs), and Fast Healthcare Interoperability Resources (FHIR) into secure and interoperable healthcare data management. Key encapsulation is performed using lattice-based algorithms, including Kyber and NTRU, and Dilithium and Falcon are algorithms used to secure digital signatures against quantum attacks. An authentication system which is based on a ZKP, and role-based access control allows privatizing access to electronic health records without exposing sensitive data. The framework is deployed on a PBFT-based permissioned blockchain and tested in simulated IoMT settings and has low latency, high throughput, and efficient cryptographic performance. In general, the suggested system will provide a reconfigurable, secure, and future-oriented method to safeguard the healthcare information against quantum threats without compromising the interoperability of the heterogeneous systems.
Open access
Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
Ensuring efficient task assignment and secure payment in mobile crowdsensing while preserving worker location privacy remains a challenging problem. Existing solutions either rely on expensive encryption schemes, employ blockchain-based verification that incurs high computational and gas costs, or use differential privacy techniques that degrade spatial accuracy. This paper introduces the Privacy-preserving Task Assignment and Payment (PTAP) framework, a lightweight solution built upon secure multi-party computation (SMPC). PTAP employs additive secret sharing and a challenge-response mechanism across three semi-honest servers to achieve anonymous task allocation and payment without blockchain or zero-knowledge proofs. The framework guarantees full unlinkability between worker identities, task locations, and payment records while maintaining accurate location-based assignment and supporting traceability for dispute resolution. Experimental evaluation using the MP-SPDZ framework demonstrates scalability to over 1.5 million workers and 7 million payment tokens. The average end-to-end completion time is approximately 35.4 seconds, with zero gas cost. Compared to the state-of-the-art AVeCQ system [15], which requires about 13 minutes and 37 MWei per transaction on the Goerli network for only 1,024 users. The results confirm PTAP's efficiency, scalability, and strong privacy guarantees for large-scale mobile crowdsensing deployments.
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.