Aug 2026· American Journal of AI Cyber Computing Management· 0 citations· 17 references
TL;DR
This research introduces a novel Unified Quantum-Resilient Blockchain-Zero Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF), which provides strong security and privacy solutions for Internet of Things networks by adopting multi-factor authentication and decentralizing identity management.
Abstract
This research introduces a novel Unified Quantum-Resilient Blockchain-Zero Knowledge Proofs Privacy Authentication Framework (QBC-ZKPAF) aimed at enhancing security in IoT environments. The system combines post-quantum cryptography, blockchain technology, and Zero Trust Architecture (ZTA) to provide secure communication, access management, and privacy-preserving authentication. It uses a Deep Q-Network Multi-Factor safe Key (DQN-MFSK) for dynamic key selection, a hybrid Reinforcement-Lattice Blockchain Key Generation for quantum-resilient key creation, and Zero-Knowledge Proofs for privacy-preserving signatures to ensure a safe Internet of Things environment. Data privacy, secrecy, auditability, traceability, and resistance to changing threats, such as quantum attacks, are all guaranteed by this architecture. Transparency and thorough post-event audit trails are supported by the blockchain ledger's immutability, which records all access attempts, data exchanges, and device interactions in an unchangeable way. Through a tracing key kept on the audit server within the Zero Trust Architecture, the architecture allows accurate source tracing in the event of suspicious activity or breaches. QBC-ZKPAF provides strong security and privacy solutions for Internet of Things networks by adopting multi-factor authentication and decentralizing identity management. The framework's efficacy is confirmed by experimental results, which show 98% privacy preservation, 700 TPS throughput, 0.98 quantum resilience, and 96% access control effectiveness, making it ideal for contemporary blockchain and IoT applications.
The rapid growth of Internet of Medical Things (IoMT)-based healthcare systems has increased the need for secure and interoperable data exchange across heterogeneous blockchain environments. Existing interoperability solutions often provide limited privacy protection and remain vulnerable to replay attacks, unauthorized access, and data inference threats. To address these challenges, this paper proposes the Attack-Resilient Secure Interoperability Protocol (ARSIP), a secure cross-chain protocol for privacy-preserving and replay-resistant communication between heterogeneous blockchain networks. ARSIP integrates four complementary security mechanisms: (i) zero-knowledge proof (ZKP)-based authentication, (ii) ECDH-based key agreement, HKDF session-key derivation, and AEAD (AES-GCM) authenticated encryption for confidentiality and integrity, (iii) nonce–timestamp-based replay protection, and (iv) dual-chain audit anchoring for traceability and auditability. To demonstrate deployment feasibility, ARSIP is implemented within an Ethereum–Hyperledger Fabric architecture supported by a cognitive serverless edge layer for adaptive workload management and low-latency processing. Experimental evaluation using a public IoMT healthcare dataset and a prototype-scale blockchain deployment shows that the ARSIP-enabled framework achieves a 30% reduction in latency, a 43% improvement in attack detection accuracy, and a 35% reduction in energy consumption compared with baseline blockchain-based healthcare systems. Formal verification and experimental analysis further support the protocol’s security properties. The results demonstrate the feasibility and effectiveness of ARSIP for secure cross-chain interoperability and indicate its potential applicability to future IoMT healthcare environments.
Kumar Velmurugan, P. Venkata, Sateesh Kumar et al.· Discover Computing· 0 citations
With the increasing demand for data privacy protection, outsourcing secure multi-party privacy intersection is faced with the dual challenges of privacy leakage and computational credibility verification. This paper proposes a blockchain-based secure multi-party privacy intersection verifiable computing scheme, which achieves the synergistic enhancement of privacy protection and computational credibility by integrating Paillier encryption algorithm, DH protocol key negotiation, HMAC-RSA authentication, Shamir secret sharing, AES encryption and zero-knowledge proof. In view of the core requirements of verifiable computing, zero-knowledge proof technology is introduced and verification logic is automatically executed through smart contracts to ensure that the calculation results are accurate and cannot be tampered with. In view of the credibility of computing nodes, a dynamic reward and punishment mechanism based on trust function and utility function is designed to constrain the honest behavior of computing parties through economic incentives. The experimental results verify the effectiveness of the scheme.
The rapid growth of the Internet-of-Medical-Things (IoMT) and quantum computing presents significant security risks. Quantum algorithms efficiently break traditional public-key encryption, exposing sensitive patient data to long-term threat. Consequently, ensuring robust authentication and long-term security under quantum threats remains a critical challenge, particularly in decentralized environments. This article proposes a decentralized two-factor authentication (2FA) protocol for IoMT. Our protocol integrates blockchain, biometric fuzzy extractors, and the BB84 quantum key distribution (QKD) protocol. We utilize the biometric fuzzy extractor to protect user identities. A public blockchain manages authentication parameters via smart contracts (SCs) to support decentralized identity verification. This mechanism facilitates user revocation and authorized traceability. Session keys establish through QKD to achieve information-theoretic security, and the protocol provides forward and backward secrecy. Formal security analysis under the Canetti–Krawczyk (CK) and real-or-random (ROR) models demonstrates resistance to impersonation, replay, man-in-the-middle, and quantum attacks. Experimental results show that our protocol reduces classical computation overhead by over 56% and classical communication overhead by 10% compared to existing baselines. Furthermore, it achieves lower communication costs while maintaining high efficiency for resource-constrained IoMT environments in the future.
Zhao-Feng Huang, Yu-Hong Ke, Xu Yang et al.· IEEE Internet of Things Jour...· 0 citations
Cloud-assisted data sharing has become the preferred approach for data exchange. Despite its popularity, the loss of physical control over outsourced data has intensified data owners’ concerns regarding data privacy and security. Existing attribute-based encryption schemes achieve fine-grained access control, but face an efficiency-security trade-off in policy updates, key privacy leakage risks, and a lack of proactive periodic integrity verification, limiting their practical application. To address these issues, this paper proposes EPPCDS, an efficient privacy-preserving cloud data sharing scheme with blockchain-assisted access control and periodic integrity checking. EPPCDS implements key-level fine-grained access control via multi-keyword searchable encryption for low-overhead secure policy updates, and adopts a double-layer digital envelope mechanism combined with certificateless cryptography and identity randomization to protect decryption tokens and user identity privacy. It also integrates provable data possession with a deposit-based reward-penalty mechanism to realize proactive periodic integrity verification. We conduct a thorough security analysis confirming EPPCDS satisfies diverse security demands in cloud data sharing. Extensive experiments on a Hyperledger Fabric prototype demonstrate that EPPCDS incurs low runtime overhead for all participating parties and outperforms representative existing schemes in terms of policy creation, policy update, and data access efficiency.
This research presents a novel decentralized identity management system leveraging Ethereum smart contracts and cryptographic protocols to enable self-sovereign digital identities. Traditional centralized identity solutions pose risks related to data privacy, security, and user autonomy. To address these limitations, we propose a blockchain-based architecture integrating smart contracts for identity governance, IPFS for decentralized attribute storage, and threshold cryptography for private data recovery. Our dual-contract model (Identity Contract and Recovery Contract) facilitates secure identity creation, verifiable attribute attestation, and robust key recovery through social consensus. We introduce a privacy-preserving mechanism using encryption-key splitting among trusted peers to enable recovery of encrypted off-chain data. Implementation uses Web3.js, Solidity, and QR-code-based communication, abstracting cryptographic complexities from end-users. Security analysis addresses replay, man-in-the-middle, Sybil, and multi-user compromise attacks with mitigations including challenge-response authentication and time-delayed contract execution. This work contributes a cryptographically secure, user-centric identity framework ensuring data sovereignty, recoverability, and interoperability within the Ethereum ecosystem.
Harika Naidu Beesabathuni· International Journal of Int...· 0 citations
Cloud computing has become the backbone of modern distributed data management, offering scalable storage and on-demand analytics to healthcare, financial, educational, and government organizations. However, two security requirements are frequently addressed in isolation in the literature: preserving the privacy of sensitive attributes during analytical processing, and guaranteeing that outsourced data remains unmodified and verifiable while it resides with an untrusted cloud service provider. This paper analyzes two complementary cloud-security paradigms adaptive differential privacy for perturbation-based confidentiality and blockchain-assisted third-party auditing for tamper-evident integrity verification and proposes a unified Blockchain-Assisted Adaptive Differential Privacy (BA-ADP) framework that integrates both into a single secure cloud data-management pipeline. The proposed framework performs sensitivity-aware adaptive noise generation to protect data confidentiality during analytics, applies lightweight encryption and access control during storage and transmission, and simultaneously anchors SHA-256 integrity hashes of every data block on a blockchain ledger that is verified through smart-contract-automated third-party auditing. This dual-layer design allows a cloud system to resist inference, linkage, and reconstruction attacks on the privacy side while resisting tampering, single-point-of-failure, and collusion risks on the integrity side, without requiring two independent security subsystems. Drawing on previously reported component-level results 96.1% privacy-protection efficiency with 3.8% leakage probability for the adaptive differential-privacy layer, and 97.4% integrity-verification accuracy with a 96.8% tampering-detection rate for the blockchain-assisted auditing layer this paper presents an architectural synthesis, a security analysis, and a discussion of the expected computational trade-offs of combining both mechanisms. The findings indicate that unifying adaptive privacy preservation with decentralized integrity auditing yields a more complete cloud-security posture than either mechanism alone, and the paper outlines the empirical validation, including full-scale testbed experiments, required before deployment.
Jayakumar D, M. Ramamoorthy· International journal of com...· 0 citations
