Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.
Open access
Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.
In the contemporary digital landscape, the demand for secure, private, and tamper-resistant communication has never been more critical. Conventional messaging platforms, which predominantly rely on centralized servers, are increasingly vulnerable to data breaches, unauthorized surveillance, and censorship. Even with the adoption of end-to-end encryption, these systems remain susceptible to single points of failure and metadata exposure, undermining user privacy and trust. Blockchain technology has emerged as a transformative solution to these challenges, offering a decentralized, immutable, and transparent infrastructure for secure data exchange. By leveraging distributed ledger technology, Blockchain-based messaging systems eliminate the need for trusted intermediaries, enhance resistance to censorship, and ensure data integrity through consensus-driven validation.
Access to digital services requires entities, such as users or software services, to establish their identities before interacting with service providers. Conventional identity management systems typically maintain separate identity records for each application, often resulting in multiple accounts for the same entity within a single service provider. When identical personally identifiable information and attributes are reused across platforms, these fragmented records can be correlated, increasing the risk of identity exposure and privacy breaches. This work presents an entity-centric identity management model tailored for cloud environments, designed to enhance privacy and reduce unnecessary information disclosure. The proposed approach is founded on two core components. The first is anonymous identification, which enables entities to interact with cloud services based on predefined privacy preferences without revealing their true identities. The second component introduces active bundles, which encapsulate personally identifiable information, usage policies, and an embedded execution environment responsible for enforcing privacy constraints. These bundles autonomously apply protection mechanisms to safeguard sensitive data, even when deployed on untrusted platforms. The proposed model offers several advantages, including reduced dependence on external identity providers, controlled disclosure of identity attributes to service providers, and secure utilization of identity data in untrusted cloud environments. By integrating privacy-enhancing technologies such as zero-knowledge proofs, the framework provides a robust and flexible solution for privacy-aware identity management in modern cloud-based systems.
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
Keyword search is a fundamental technique for retrieving data outsourced to the cloud. Although encryption preserves data confidentiality, existing searchable encryption schemes often fail to efficiently support dynamic authorization and flexible retrieval. To address these limitations, we propose BAMKS , a blockchain-assisted attribute-based multi-keyword search scheme that supports secure and efficient search over version-aware encrypted data. In BAMKS , multiple data owners collaboratively generate version-bound access tokens that grant authorized users decryption privileges over evolving data. The scheme further enables conjunctive keyword search with updatable indexes. To ensure the integrity of search results, users can verify their correctness using an aggregated Schnorr-based non-interactive zero-knowledge proof, which is validated by smart contracts. In addition, BAMKS provides efficient attribute and user revocation without re-encrypting the stored ciphertexts, and supports user traceability for identifying malicious users from leaked keys. We formally prove that BAMKS achieves security against chosen-plaintext attacks (IND-CPA) and chosen-keyword attacks (IND-CKA) under the Decisional Bilinear Diffie-Hellman (DBDH) assumption. Performance evaluations show that the scheme achieves lightweight decryption and efficient multi-keyword search, thereby reducing client-side computation and making it suitable for resource-constrained IoT environments. These features demonstrate the practicality of BAMKS for distributed cloud-edge-IoT storage applications.
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 key–value store encoding (OKVS) mechanism is introduced to enable secure and efficient key–value mapping and decoding, thereby reducing communication overhead. Under the malicious security model, MESA further incorporates non-interactive zero-knowledge proof (NIZKP) to verify the consistency and validity of results submitted by clients, effectively preventing data forgery and disruption attacks. Experimental results and analysis demonstrate that MESA provides strong privacy guarantees while achieving high computation and communication efficiency in deployments involving multiple untrusted clients.
This publication introduces Zero-Knowledge Behavioral Proof (ZKBP) as a post-biometric authentication primitive designed for the QADMON canonical security framework. ZKBP replaces traditional biometric and password-based identity with cryptographically verifiable behavioral continuity. The protocol proves liveness, integrity and continuity of behavior without revealing biometric templates, raw behavioral signals, or any permanent human identifier. The package includes: - Formal cryptographic definition of ZKBP - Security proofs under LWE-based post-quantum assumptions - Comprehensive threat model (AI imitation, replay, side-channels, insider threats) - Protocol specification in JSON - Comparative security tables (CSV) - Multilingual human-readable documentation (EN, RU, HE, ZH, AR) - Implementation notes for PQC + TEE environments This module follows the canonical QADMON axiom: FSIG ≠ Cryptographic Key FSIG = Zero-Knowledge Behavioral Proof The only cryptographic secret is a post-quantum key stored inside a Trusted Execution Environment (TEE). This work is published as Module 02 of the QADMON Canonical Security Framework.
The increasing need for trustworthy digital document verification presents challenges in ensuring authenticity, transparency, and tamper resistance without relying on centralized authorities. This study aims to develop and evaluate a decentralized document notarization system using Ethereum and IPFS that offers secure, transparent, and cost-efficient verification. The system employs modular smart contracts deployed through a factory pattern to create user-specific verifier instances, enabling document submission, revocation, and verification using keccak-256 hashes, ECDSA signatures, and IPFS content identifiers. Methods include contract development, deployment on a local Hardhat network, performance benchmarking, and front-end integration for user interaction. Results show that verifier deployment consumes approximately 1.19 million gas (≈$85 at 20 gwei), document submission around 85 thousand gas (≈$6), and revocation about 50 thousand gas (≈$3.50). Client-side operations such as hashing and IPFS pinning occur in under 50 milliseconds, while real-world blockchain confirmations take 10–30 seconds. The findings demonstrate that decentralized notarization using Ethereum and IPFS is both technically feasible and economically viable. Future enhancements, including Layer 2 rollups, batch notarization, and privacy-preserving features such as encrypted IPFS pinning or zero-knowledge proofs, are proposed to further improve scalability, cost-efficiency, and data confidentiality
Open access
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
We introduce the Y.I.N. Mazari Ordering, a fundamental primitive for achieving verifiable differential privacy in federated learning systems. The ordering (noise → proof → encrypt → aggregate) is proven to be necessary—no efficient alternative exists—and universal across all encryption schemes, proof systems, and aggregation topologies. Patent pending: US 63/923,348, US 19/399,646, US 19/403,244 Keywords: Verifiable Differential Privacy, Federated Learning, Zero-Knowledge Proofs, Homomorphic Encryption, Privacy-Preserving Machine Learning
In a rapidly digitalizing world, identity verification has become the cornerstone of secure online interaction. Traditional authentication models, which depend on centralized authorities and password-based systems, are increasingly vulnerable to breaches, identity theft, and data manipulation. Blockchain-backed identity systems offer a promising alternative by decentralizing trust, ensuring immutability, and empowering users with self-sovereign control over their credentials. This review explores how blockchain technology enhances authentication reliability through decentralization, cryptographic assurance, and automation. The paper first examines the fundamentals of blockchain-based identity management, including decentralized identifiers (DIDs), verifiable credentials (VCs), and smart contracts that automate credential verification and revocation. It then presents the architectural components of blockchain identity systems, highlighting how cryptographic hashing, distributed consensus, and off-chain storage combine to create secure yet compliant authentication workflows. The analysis demonstrates that blockchain-backed identity frameworks significantly improve authentication reliability by removing single points of failure, enhancing data integrity, and enabling privacy-preserving verification through mechanisms like zero-knowledge proofs. Comparative evaluation with traditional systems reveals that blockchain ensures superior resilience, transparency, and user control, albeit with challenges in scalability, interoperability, and key management.
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.
Public companies and institutional investors that hold Bitcoin face increasing pressure to show solvency, manage risk, and satisfy regulatory expectations without exposing internal wallet structures or trading strategies. This paper introduces the Treasury Proof Ledger (TPL), a Bitcoin-anchored logging framework for multi-domain Bitcoin treasuries that treats on-chain and off-chain exposures as a conserved state machine with an explicit fee sink. A TPL instance records proof-of-reserves snapshots, proof-of-transit receipts for movements between domains, and policy metadata, and it supports restricted views based on stakeholder permissions. We define an idealised TPL model, represent Bitcoin treasuries as multi-domain exposure vectors, and give deployment-level security notions including exposure soundness, policy completeness, non-equivocation, and privacy-compatible policy views. We then outline how practical, restricted forms of these guarantees can be achieved by combining standard proof-of-reserves and proof-of-transit techniques with hash-based commitments anchored on Bitcoin. The results are existence-type statements: they show which guarantees are achievable once economic and governance assumptions are set, without claiming that any current system already provides them. A stylised corporate-treasury example illustrates how TPL could support responsible transparency policies and future cross-institution checks consistent with Bitcoin's fixed monetary supply.
The purpose of this study is to address the persistent security and privacy challenges in cloud-based Electronic Health Record (EHR) sharing by proposing a blockchain-enabled architecture that integrates decentralized storage and smart contracts. Traditional mobile cloud solutions improve data accessibility but rely on centralized control, making them vulnerable to unauthorized access, single points of failure, and limited patient transparency. To overcome these limitations, this research designs a user-centric access control framework that leverages the Ethereum blockchain, smart contracts, and the InterPlanetary File System (IPFS) within a mobile cloud environment. The methodology involves developing and deploying a prototype on Amazon Web Services, supported by an Android-based mobile application that enables healthcare providers and patients to interact with the blockchain network. Experimental evaluation was conducted using wearable sensor data to test the performance, scalability, and resilience of the proposed system. The findings indicate that the framework ensures secure EHR exchange, enforces fine-grained access policies, and achieves reduced latency compared to conventional centralized approaches. Unauthorized requests were reliably detected and blocked through the smart contract mechanism, while authorized users accessed records with minimal delay. The results also confirm the lightweight overhead of the system, making it practical for mobile healthcare environments. The practical implications of this work lie in offering a tamper-resistant, transparent, and patient-centric solution for medical data sharing, thereby improving trust, reducing administrative overhead, and supporting real-time healthcare services in distributed and resource-constrained settings.