Smart contract vulnerability detection has gained increasing attention due to growing financial losses from hacker attacks. Existing deep learning methods either rely on a single feature type or lack effective interaction among heterogeneous features, limiting vulnerability representation. To address this, we propose neighborhood constrained cross attention. It uses the control-flow graph’s k-hop neighborhood as a structural prior to restrict bidirectional interactions between graph features and sequence features to local regions likely associated with the same execution logic, thereby reducing noise from global attention. Self-attention is further applied within each branch to model long-range dependencies. Experiments show that NCCA-Det achieves accuracies of 94.87%, 92.62%, and 92.94% on three common vulnerability types, significantly outperforming comparative methods, and thus offers a complementary solution for bytecode-level smart contract vulnerability detection.
The rapid evolution of cloud computing has revolutionized digital data storage and sharing, enabling users to access information anytime and anywhere. Despite these advantages, cloud-based systems face major challenges related to data security, privacy protection, and trust management, particularly when handling sensitive user information. Conventional cloud storage solutions operate on centralized architectures, where a single cloud service provider manages and controls the stored data. This centralized model introduces significant risks, including single points of failure, unauthorized data access, data manipulation, and limited visibility into data-sharing activities. In many traditional systems, data protection mechanisms rely on basic encryption methods without strong auditing or verification features, leaving them vulnerable to insider attacks and external cyber threats. Furthermore, the absence of immutable transaction records and robust key management practices reduces accountability and weakens user confidence in cloud environments. To address these shortcomings, the proposed system presents a secure cloud data sharing framework that combines Elliptic Curve Cryptography (ECC) with blockchain technology. In this approach, user files are encrypted using ECC before being uploaded to the cloud, ensuring strong data confidentiality and protection against unauthorized access. Simultaneously, blockchain technology is employed to record file metadata and transaction details in a decentralized and tamper-resistant ledger, enabling transparent and verifiable audit trails. The decentralized architecture eliminates reliance on a single authority, enhances trust, and prevents unauthorized modification of stored records. Additionally, secure authentication and controlled access mechanisms further reinforce system security. By integrating advanced cryptographic encryption with decentralized verification, the proposed solution enhances data integrity, improves transparency, and establishes a reliable and accountable framework for secure cloud data sharing.
Privacy-preserving aggregation is a cornerstone for AI systems that learn from distributed data without exposing individual records, especially in federated learning and telemetry. Existing two-server protocols (e.g., Prio and successors) set a practical baseline by validating inputs while preventing any single party from learning users' values, but they impose symmetric costs on both servers and communication that scales with the per-client input dimension $L$. Modern learning tasks routinely involve dimensionalities $L$ in the tens to hundreds of millions of model parameters. We present TAPAS, a two-server asymmetric private aggregation scheme that addresses these limitations along four dimensions: (i) no trusted setup or preprocessing, (ii) server-side communication that is independent of $L$ (iii) post-quantum security based solely on standard lattice assumptions (LWE, SIS), and (iv) stronger robustness with identifiable abort and full malicious security for the servers. A key design choice is intentional asymmetry: one server bears the $O(L)$ aggregation and verification work, while the other operates as a lightweight facilitator with computation independent of $L$. This reduces total cost, enables the secondary server to run on commodity hardware, and strengthens the non-collusion assumption of the servers. One of our main contributions is a suite of new and efficient lattice-based zero-knowledge proofs; to our knowledge, we are the first to establish privacy and correctness with identifiable abort in the two-server setting.
ABSTRACT To resolve the conflict between privacy protection and compliance verification of commercial contracts in the digital economy, this study aims to propose the application of a commercial contract generation technology based on verifiable encryption signature algorithms in civil and commercial law. This technology innovatively combines Attribute‐Based Encryption (ABE) with Zero‐Knowledge Succinct Non‐Interactive Argument of Knowledge (zk‐SNARKs) to construct a new electronic contract framework. Methodologically, ABE is used to encrypt contract clauses, enabling refined “on‐demand decryption” based on the roles and permissions of participants. Meanwhile, zk‐SNARKs are utilized to generate proofs for clauses requiring verification (e.g., transaction amount), allowing third parties (such as regulatory authorities) to complete “privacy‐preserving verification” without accessing the original contract text. Experimental results show that when the model processes a contract containing 500 clauses, the size of the generated data package is only 64.7 KB, which is approximately 3.8 times smaller than that of the pure ABE encryption model (247.8 KB) and 7.4 times smaller than that of the traditional digital signature model (480.1 KB). At the same time, the key size for users to access 500 clauses is only 5.2 KB, which is much smaller than that of the baseline models. Therefore, this technical solution effectively resolves the long‐standing “privacy‐verification” dilemma in commercial activities. This also proves its feasibility and economy in practical applications with the advantages of low storage and transmission costs, providing secure and reliable technical support for civil and commercial legal practices in the digital era.
Filippo Scaramuzza, Marco Tonnarelli, Damian A. Tamburri, W.J.A.M. van den Heuvel
The challenge of achieving auditable, GDPR-compliant consent management while preserving true data subject privacy persists, as current blockchain-based solutions compromise anonymity through public ledgers. This paper addresses this by introducing a novel, privacy-by-design IT artefact built on the Ethereum platform that employs Zero-knowledge Succinct Non-Interactive ARgument of Knowledge (zk-SNARKs) to enable truly anonymous and irrefutable validation of data processing permissions. Implemented and evaluated through a Design Science Research (DSR) approach, the artefact demonstrated a high degree of functional and GDPR compliance, confirming its capacity to generate trustless, auditable on-chain proof of permission status. The proof of concept successfully implements core privacy-by-design principles through anonymity and encryption, with technical benchmarks indicating acceptable performance for the artefact's intended use despite the computational intensity of proof generation.
Modern digital financial systems continuously face the fundamental trilemma of transaction confidentiality, computational efficiency, and cryptographic security. While existing blockchain technologies have made significant progress in security and decentralization, they are limited in ensuring confidentiality of financial information due to their public distributed ledger structure. To overcome these limitations, we present Epsilon, an innovative protocol that fundamentally addresses this complexity by directly combining Elliptic Curve-based Partially Homomorphic Encryption with parallel processing architectures. This paper introduces the Cryptora Protocol, a complete end-to-end operational framework that implements a two-tiered architecture where transaction processing is performed off-chain in a peer-to-peer manner, with results recorded on smart contract-based on-chain distributed ledgers. Our experimental results demonstrate that Epsilon achieves 500,000 TPS with end-to-end latency of 50-100ms while maintaining strong cryptographic guarantees including semantic security, unlinkability, and double-spending resistance.
We present COMET-MPC (Commitment-Oriented Multi-Party Computation with Equality Testing), a novel MPC framework that fundamentally reimagines multi-party computation as a commitment verification problem rather than a traditional secret-sharing problem. COMET-MPC achieves dealerless, one-round setup by reducing all MPC coordination tasks—input binding, consistency verification, and replay prevention—to homomorphic equality testing over elliptic-curve commitments. Unlike classical MPC protocols (Shamir’s secret sharing, FROST) that require multi-round polynomial verification or interactive zero-knowledge proofs, COMET-MPC performs verification through a single algebraic check: testing whether a group element equals the identity. This zero-detection paradigm exploits the structural identity between EC-Pedersen commitments and EC-ElGamal ciphertexts, enabling decrypt-free verification with perfect correctness and computational hiding under the Decisional Diffie-Hellman (DDH) assumption. We provide complete algorithmic specifications, formal security proofs including binding, hiding, replay resistance, and a rigorous security level analysis demonstrating ≈ 128-bit security for standard 256-bit elliptic curves. COMET-MPC is particularly suited for real-world ledger systems, authentication protocols, confidential databases, and privacy-preserving regulatory compliance where minimal interaction, minimal trust, and minimal leakage are paramount.
Recently, developing technologies for smart cities, although scalable and cost-effective, have been challenging to provide anonymous verification and on-chain integrity with low overhead due to the increasing attack surface. We propose ZkPSLB, a layered end-to-end security framework to address the problem. ZkPSLB utilizes a Zero-Knowledge Concise Non-Interactive Knowledge Argument (zk-SNARK), a type of Zero-Knowledge Proof (ZKP) scheme, embedded within the Constrained Application Protocol (CoAP) for anonymous device authentication. Sensor payloads are encrypted with elliptic curve cryptography (ECC) and stored in a decentralized cloud storage system (IPFS). IPFS CIDs are committed to the chain, ensuring both off-chain confidentiality and on-chain integrity. In the evaluation conducted with 500 devices/5000 metadata, the authentication communication overhead was measured at 1952 bits. The event-based smart contract (EBSC) reduces on-chain payload and gas growth compared to storage-based designs, and its cost advantage has been validated.
Seung Kwon Lee, Seok Bin Son, Joongheon Kim, Hoh Peter In
Quantum machine learning (QML) has attracted growing interest for their ability to achieve superior performance with significantly fewer parameters. However, the high cost and scarcity of current hardware push inference to cloud-hosted quantum devices, creating a tension between verifiability and confidentiality. This work proposes a novel framework that converts quantum neural network operations into classical arithmetic circuits that faithfully approximate genuine quantum computations. By encrypting these circuits with zero-knowledge proofs, it ensures computational validity while concealing internal parameters. Experimental results show that our classical circuits achieve fidelity above 0.9996 and total variation distance below 1% compared to actual quantum computations, verifying the practicality of trustworthy and privacy-preserving quantum inference.
Open access
Quantum Computing Algorithms and Architecture
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract E-commerce platforms are increasingly targeted by sophisticated cyber-attacks that exploit the inherent vulnerabilities of centralised authentication architectures. Password-based systems, two-factor authentication, and centralised identity stores have demonstrated persistent susceptibility to phishing, credential stuffing, man-in-the-middle interception, and large-scale data breaches. This paper investigates the design, implementation, and evaluation of a blockchain-based authentication system as a structural response to these limitations. The proposed system leverages Ethereum’s public-key cryptographic infrastructure, MetaMask wallet integration, Web3.js, JSON Web Tokens (JWT), React.js, and Node.js to deliver a decentralised, tamper-proof, and privacy-preserving authentication flow for e-commerce applications. A proof-of-concept prototype was built and evaluated against conventional authentication methods across eleven analytical dimensions, including security architecture, data integrity, identity management, scalability, trust models, and regulatory alignment. Results confirm that the blockchain-based approach eliminates credential database attack surfaces, enables non-repudiable transaction signing, supports Zero-Knowledge Proof (ZKP) verification, and implements Self-Sovereign Identity (SSI) principles that return data ownership to users. Scalability under high transaction volumes and user onboarding complexity are identified as the primary adoption barriers, suggesting that hybrid architectures may offer the most pragmatic near-term deployment pathway. The study contributes an empirically grounded, real-world implementation perspective to the growing literature on blockchain security applications, and provides actionable guidance for e-commerce operators, security practitioners, and researchers exploring decentralised identity systems. Keywords Blockchain Authentication, E-Commerce Security, Ethereum, Metamask, Decentralised Identity, Zero-Knowledge Proofs, Self-Sovereign Identity, JWT, Smart Contracts, Credential Stuffing, Public-Key Cryptography.
Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without sharing representative evaluation data with vendors, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero-Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, cryptographically verifiable competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Vendors cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation, while the on-chain workflow automates escrow, proof verification, and best-vendor selection with arbiter-based dispute resolution. ZKP privacy is scoped to vendor model weights; operator-side evaluation-data confidentiality is managed separately via synthetic, de-identified, or public benchmark data. We analyze three ZKP workflow variations and evaluate them on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation, while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling.
Abstract: This paper introduces an innovative access control architecture based on a dual-blockchain framework that distinctly separates access management from data storage to enhance system security, scalability, and privacy. Architecture employs a primary blockchain to manage user authentication and enforce dynamic, fine-grained permissions through smart contracts. In parallel, a secondary, isolated blockchain is used exclusively for storing sensitive data, which can only be accessed following successful authorization on the primary chain. To ensure data integrity and tamper resistance, the system utilizes the SHA-256 cryptographic hash function for securing access logs and verifying data authenticity across both blockchains. The two chains are securely interconnected using Hyperledger YUI, which facilitates reliable inter-chain communication while maintaining a decentralized structure. A proof-of-concept implementation using Ethereum-based blockchains demonstrates the system's capability to enforce secure, dynamic access controls across chains. Overall, the proposed architecture overcomes key limitations of conventional blockchain systems by enhancing modularity, strengthening governance, and providing a robust, adaptable framework suitable for data-sensitive applications requiring strict regulatory compliance.
Tourism relies on central review platforms which produces three major systemic issues that include fake content, unclear moderation activities and inadequate compensation systems for authentic consumer contributions. TrustChain resolves industry review challenges using a blockchain formation that combines Layer-2 scaling solutions with zero-knowledge proofs (ZKPs) and tokenized governance system. The transaction cost reductions are huge following the implementation of a Proof-of-Stake consensus system on the Polygon-based architecture although the system maintains confirmation times shorter than 2 seconds. The implementation of Self-Sovereign Identity framework alongside transaction-linking smart contracts maintains highly accurate review authenticity in prototype evaluations through TripAdvisor datasets. Through its DAO governance structure users can verify review authenticity by using multi-signature checks which resolve all major disputes in less than one day. The integration of IPFS for multimedia storage generates an 83% decline in blockchain bloat that does not affect cryptographic security.
Ankit Kumar, Andres J. Aparcana-Tasayco, Minjung Kim, David Camacho · 5 authors
The expansion of Internet of Things (IoT) devices brings challenges of data security and privacy preservation in critical infrastructure. The proposed system combines blockchain technology with federated learning (FL) to secure IoT communications. It ensures decentralized model training with immutable and verifiable blockchain records. The study incorporates a lightweight FL model with a two-stage multitask head for binary and multiclass attack detection, enabling efficient deployment in constrained IoT. Federated learning effectively resolves privacy issues by facilitating cooperative model training across dispersed IoT nodes without revealing raw data. It guarantees collaboration based on a trust-aware mechanism that evaluates the reliability of clients and guides the robust aggregation. Blockchain ensures tamper-evident auditing of model updates and supports Byzantine-fault-tolerant (BFT) and Delegated proof-of-stake (DPoS) trust guarantees. Blockchain records cryptographic hashes of model modifications in a tamper-proof ledger, ensuring the legitimacy of the training process. Smart contracts enable the tamper-evident logging of model hashes, and global model convergence is ensured by federated averaging. Experimental tests demonstrate a secure and verifiable collaborative learning enabling model integrity in IoT networks. The study achieved a fast block generation time of 77.3ms, satisfactory model performance with 98% of training accuracy, and 0.992 F1-score alongside meaningful evolution of client trust values. The final testing accuracy of the FL model for the binary class detection is 98.1%. In a multi-class attack scenario, the FL model achieves a strong multi-class attack detection rate for dominant attack types.
Asmart-contract framework for patient identity management in digital health platforms. A major gap in current digital health ecosystems is the absence of a portable and verifiable patient identity layer across fragmented electronic health record (EHR) systems. The problem addressed is the lack of a portable, verifiable, and patient-centric identity layer across fragmented electronic health record systems, which weakens access accountability and privacy. The proposed solution couples fast healthcare interoperability resources (FHIR) with self-sovereign identity (SSI), storing FHIR payloads off-chain in the InterPlanetary file system (IPFS) and committing only encrypted pointers and policies on Polygon smart contracts. Patient identifiers and content addresses are protected with AES-256 GCMauthenticated encryption and elliptic-curve key wrapping (ECIES) for both the healthcare administrator and the patient. A web implementation in Next.js using thirdweb automates wallet creation, keystore handling, encryption, and on-chain commits. In evaluation with 50 synthetic registrations, success reached 100 percent, median end-to-end latency was 5.86 seconds, mean on-chain latency 3.77 seconds, average transaction fee 0.0401 POL/MATIC, encryption time 13.9 milliseconds, and all decryptions validated. The results indicate practical feasibility for portable identity and auditable access, with on-chain latency as the main bottleneck to be reduced through batching, cheaper layers, and broader field trials. However, this study is limited because the evaluation uses only synthetic data and singleprovider testing, without real-world patients or multi-institutional environments. Zero-knowledge proofs (ZKP) are discussed conceptually as future integration and are not implemented or benchmarked in this work.
Distinguishing between benign and poisoned gradients hidden behind cryptographic masks is a critical challenge in privacy-preserving federated learning (FL). Existing robust aggregation defenses suffer from two primary limitations: (1) susceptibility to manipulation, where adversaries induce deviations from standard protocols to bypass statistics-based defenses (e.g., mean or median), and (2) limited detection granularity, where the reliance on coarse statistics under encryption fails to identify subtle or coordinated poisoning behaviors. To address these issues, we propose RankFL, a poison-robust and privacy-preserving FL scheme that leverages order sorting over ciphertext gradients. RankFL utilizes an efficient Paillier-based two-party comparison protocol to construct a joint order tree, facilitating quartile-driven filtering of malicious updates without compromising individual gradient privacy. Furthermore, we introduce RankFL-Extend, which incorporates zero-knowledge proof-of-knowledge and bidirectional verification to secure the ranking process against active adversaries. We provide a rigorous theoretical analysis to establish the scheme's privacy, indistinguishability, and convergence guarantees. Extensive experiments across diverse datasets and attack scenarios demonstrate that the proposed scheme achieves a$3\%$accuracy improvement over state-of-the-art defenses under poisoning attacks.
B. Siva Ganesh, Aditya Basantia, Soumya Sambit Mishra, Jagdish Behera · 5 authors
Healthcare systems are essential for patient care and data management but face persistent challenges with data security, interoperability, and transparency. Traditional centralized storage models increase data breach risks and limit patients’ control over personal health information, complicating seamless data sharing across providers. However, centralized storage systems still lack the security and interoperability required for robust data sharing. To address this, our study proposes a ZKP-based verification system integrated with NFT blockchain technology, aimed at enhancing identity verification while ensuring data privacy and patient control. In our method, NFTs serve as unique digital identifiers linked to patient records, while zero-knowledge proofs (ZKPs) confirm data ownership without disclosing sensitive information. Our findings indicate that this approach mitigates identity fraud risks, strengthens data security, and offers a patient-centered system for selective data access. Beyond patient records, our solution supports transparency in clinical trials and pharmaceutical supply chains, combating counterfeiting and improving quality control. In our paper, we proposed a ZKP-based verification system integrated with NFT blockchain technology to improve identity verification in healthcare, offering a robust, patient-centered approach to data security and integrity.
2 source records
Blockchain Technology Applications and Security
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
Existing high performance blockchains verify one signature per transaction on the critical path, which creates O(N) verification cost, high hardware pressure, and difficult post quantum migration. This paper presents ACE Runtime, a ZKP native execution layer built on identity authorization separation. We replace per transaction signature checks with lightweight HMAC attestations in the hot path, then generate one aggregated zero knowledge finality certificate per block in an asynchronous prove stage. The system is organized as an Attest Execute Prove pipeline with two tier finality: soft finality from BFT voting and hard finality from proof verification. Under standard cryptographic assumptions, we provide formal arguments for attestation unforgeability and hard finality irreversibility. We also define a two phase timeout and backup proving path with witness availability gossip for liveness under builder failure. Quantitative results combine analytical modeling with reference implementation measurements. The prototype shows low CPU orchestration overhead, while model driven analysis projects constant per block verification cost, lower validator hardware requirements for non builders, and better bandwidth efficiency than per transaction signature designs. These results indicate that identity authorization separation is a practical architecture for sub second cryptographic finality with a clear path toward stronger post quantum components.
Post-quantum signature schemes impose kilobyte-scale on-chain artifacts. Verifying them inside ZK circuits merely relocates the cost via expensive lattice arithmetic in prover circuits. We present ZK-ACE (Zero-Knowledge Authorization for Cryptographic Entities), which replaces transaction-carried signature objects with identity-bound ZK statements. Given a deterministic identity derivation primitive (DIDP) as a black box, the prover demonstrates in zero knowledge that an identity consistent with an on-chain commitment authorized the transaction; no signature object is produced or verified on-chain. We provide game-based definitions and reduction-based proofs for authorization soundness, replay resistance, substitution resistance, and cross-domain separation, under knowledge soundness, collision resistance, and DIDP recovery hardness. Structural data accounting shows an order-of-magnitude reduction in per-transaction authorization data versus direct PQC deployment. A reference implementation offers two backends: Circle STARK (341 active rows / 361 AIR constraint expressions, 14.5 ms prove, 1.1 ms verify, approx. 107 KB proofs, transparent setup, post-quantum-oriented) and Groth16/BN254 (2,155 R1CS constraints, 37.3 ms prove, 128-byte proofs). Both are roughly 500--2,300x smaller than in-circuit PQC signature verification. Under mandatory per-block STARK aggregation, per-transaction consensus-visible data is approx. 160 bytes.