The growing number of connected devices creates a strong demand for secure and private identity management across different networks. Conventional centralized systems suffer from a single point of failure, while many decentralized, blockchain-based solutions struggle to balance scalability, functional versatility, and privacy protection. To bridge these gaps, we propose a Blockchain-based Lightweight Dual-mode Authentication (BLDA) mechanism. BLDA introduces two distinct authentication pathways: the first achieves constant-time verification based on a dynamic cryptographic accumulator integrated with zero-knowledge proofs (ZKPs), offering optimal efficiency and unlinkability for simple membership checks. The second enables logarithmic-time verification based on a Merkle Patricia Trie (MPT) and ZKPs, providing efficient and privacy-preserving attestation of specific user attributes. Both modes ensure minimal information disclosure during authentication. A security and complexity analysis demonstrates that BLDA provides a secure and efficient framework, well-suited for large-scale applications requiring efficient cross-domain authentication.
Privacy-preserving computation enables multiple parties to jointly compute a function while keeping their inputs private. Protocols designed for the semi-honest model achieve high efficiency by assuming participants will correctly follow the protocolās cryptographic steps. However, this security assumption is confined to the protocolās internal execution, creating a crucial accountability gap. It offers no inherent method to prove that the inputs and function used in the computation actually align with what was externally agreed upon. In this paper, we introduce a novel framework that enhances privacy-preserving computation with public verifiability and accountability, while maintaining composability. Our framework leverages a blockchain as an immutable trust anchor to register cryptographic commitments of both participant inputs and the functionās specification. We then employ a zero-knowledge proof protocol to verify that the privacy-preserving computation is performed correctly using the committed data and function logic. The security of our model is formally proven to guarantee both input privacy and computational integrity, while our performance evaluation shows its practical scalability.
Decentralized anonymous credentials (DACs) enable users to prove possession of specific identity attributes without disclosing additional information or relying on a centralized authority. However, existing DAC schemes commonly rely on complex zero-knowledge proofs, resulting in high computational overhead. They also lack sufficient flexibility and efficiency in handling multi-authority environments and supporting complex access policies, while facing limitations in trust assumptions and scalability. To address these challenges, this paper proposes a novel threshold anonymous credential scheme. Specifically, we introduce a ciphertext-policy attribute-based encryption (CP-ABE) scheme that supports threshold key distribution and aggregation, and leverage it to construct the threshold anonymous credential scheme. Experimental results and security analysis demonstrate that the proposed scheme exhibits high efficiency and flexibility in constructing authentication for complex access policies.
Democratic institutions increasingly rely on verifiable digital trust to enable fair participation and evidence-based decisions. Truvry is a decentralised protocol that converts behaviour-based evidence (usage patterns, transaction integrity, peer attestations) into portable cryptographic proofs that remain independent of any single platform or identifier, allowing individuals to transfer trust capital across domains while preserving privacy. The current prototype is zero-knowledgeācompatible; in this version we use hashed proof anchoring and field-level redaction (no zk-SNARK module is deployed), with configurable smart-contract verifiers. By decoupling trust from identity, Truvry widens citizen inclusion, mitigates gatekeeping bias, and supplies auditable inputs for AI-mediated governance. In prototype tests (n=112), end-to-end proof issuance averaged 3.7 s (fastest local 1.4 s), verifier parse+check averaged 1.8 s, and the current minimum anonymisation entropy is 8.9 bits; gas costs for optional on-chain anchoring remained below US$0.02. All results are based on simulated user streams; a production pilot is planned.
In the context of cultural resource big data sharing and trading, existing practices face challenges such as coarse-grained permission management, privacy leakage during data delivery, and insufficient process automation. To address these issues, this paper proposes an integrated solution for permission management and automated transaction delivery. First, a multi-dimensional attribute permission model (MDAPM) for cultural resources is introduced, which leverages fuzzy mathematics and smart contracts to achieve dynamic authorization across the three dimensions of user, resource, and context, while incorporating zero-knowledge proofs (ZKP) to mitigate privacy leakage in permission verification. Second, a permission-data-address coupled delivery framework (PDACPF) is designed, which integrates the consortium blockchain and the interPlanetary file system (IPFS) for distributed storage to enable end-to-end automation of cultural resource data management, from preprocessing and transaction triggering through secure delivery. Simulation results demonstrate that under a lOO-concurrency scenario, the solution achieves a permission adjustment response time of less than 0.8 s, a data delivery success rate of at least 99.9%, and reduces privacy leakage risk to zero, thereby effectively supporting standardized trading and sharing of cultural resource big data.
Message spoofing and denial-of-service (DoS) attacks threaten vehicular network security by disrupting communication channels and falsifying safety-critical data. Traditional intrusion detection systems (IDS) exhibit high computational overhead and limited adaptability to evolving attack patterns. This paper presents a hybrid security framework integrating Language Agent Models (LAM) with a dual-layer blockchain architecture for real-time threat detection in Internet of Vehicles (IoV) networks. The LAM operates on edge devices to analyze heterogeneous data streams from CAN bus, V2X, and GPS sources. It identifies spoofing and DoS anomalies through transformer-based attention mechanisms with fewer than 1 billion parameters. The dual-layer blockchain combines Proof-of-Authority-and-Association (PoA2) consensus at layer 1 with zero-knowledge rollup (zk-Rollup) at layer 2. This architecture ensures tamper-proof alert logging while reducing on-chain storage overhead. The PoA2mechanism employs pre-authenticated validators to achieve microsecond-scale transaction finality. The zk-Rollup layer aggregates alert transactions into cryptographic validity proofs, minimizing blockchain storage requirements. Performance evaluation demonstrates the frameworkās effectiveness across multiple metrics. Detection accuracy reaches 95.7% on the CICIoV2024 dataset and 96.9% on the Car-Hacking dataset. Precision exceeds 97% with F1-scores above 95% on both benchmarks. The system maintains false positive rates below 5.2%. End-to-end response latency remains under 5 milliseconds (ms), meeting real-time safety requirements. The blockchain layer processes over 2,187 transactions per second with 25 validator nodes. Storage optimization achieves a 92% reduction in on-chain data volume. Energy consumption decreases by 4.3 times compared to cloud-hosted language models. The proposed architecture provides deterministic threat detection with cryptographic auditability for large-scale IoV deployments.
The paper explores the possibility of expanding the use of end-to-end encryption protocols based on the Double Ratchet algorithm in applications with low trust in the server, particularly in turn-based games and strategic interactions. The relevance of the research is due to the growing need for secure communication in cyberattacks, especially during military operations. The field of end-to-end encryption requires the study of additional applications beyond the usual ones, such as encrypted communication in text messengers. The developed implementation of the protocol can be safely used in any applications that aim to implement end-to-end encryption and satisfy the criterion of session ephemerality (in cases where secrets are stored outside a secure environment). The implemented server supports ephemeral sessions, which guarantee minimal risks of information compromise, and uses digital signatures (EdDSA) for user authentication. Logical routing of requests ensures efficient message transmission in secure scenarios. The choice of the classic game of checkers as an example allowed the authors to effectively demonstrate the advantages of end-to-end encryption and the capabilities of the implemented protocol. All cryptographic operations, including key generation, encryption and decryption of messages, are successfully performed on client devices. It is important to improve error handling mechanisms and optimize the operation of WebAssembly. An interesting area of further research is the creation of zero-knowledge proof mechanisms to prevent Man-In-The-Middle attacks during the creation of a shared secret, optimizing integration with cryptographic hardware security modules (HSM), and exploring the scalability of the solution. The proposed approach can be used to solve real-world information security problems where trust in the data transmission channel is critically important. Thus, the work has created a comprehensive solution that includes a cryptographic protocol, a backend, and a web client, which demonstrates the viability of end-to-end encryption in browser environments and multiplayer games. The work can be used as a basis for further research and development in the field of security of communication systems and privacy in multiplayer games.
Graph-structured data has become central to modern analytics, enabling institutions to model relationships in domains such as healthcare, finance, cyber security, and education. However, privacy regulations and institutional policies restrict the sharing of sensitive nodes, edges, or interaction logs, preventing the discovery of global graph patterns. This paper introduces a novel framework for Federated Graph Pattern Mining Across Institutions (FGPM-AI), enabling multiple organizations to collaboratively extract global sub graphs, motifs, and temporal patterns without sharing raw graph data. The framework proposes six novel contributions: (1) Privacy-Preserving Pattern Signatures (PPPS) for anonymized sub graph encoding, (2) Federated Temporal Graph Pattern Mining (FT-GPM) to learn evolving patterns across distributed graphs, (3) Zero-Exchange Federated Sub graph Matching (ZE-FSM) using zero-knowledge proofs, (4) Heterogeneity-Aware Graph Pattern Consensus (HGPC) for semantic alignment between distinct graph schemas, (5) Communication-Adaptive Pattern Sharing (CA-FGM) for bandwidth-efficient collaboration, and (6) Multi-Party Graph Pattern Distillation (MGPD) for merging patterns into a unified knowledge model. Experimental design considerations demonstrate the feasibility and robustness of the framework. The results highlight FGPM-AI as a promising direction for secure, scalable, and intelligent cross-institution graph analytics.
Data protection in smart agricultural markets remains a critical area of inquiry amid escalating digital transformations. This chapter examines the challenges and solutions surrounding privacy in agricultural data ecosystems, with particular attention to encryption, blockchain, and federated learning technologies. It also evaluates global legal, ethical, and regulatory frameworks such as the GDPR and CCPA that influence data governance in digital agriculture. As of 2023, over 75% of large-scale farms in developed nations employ IoT and AI-driven tools for data collection, yet only 38% have robust data protection measures in place, exposing vulnerabilities in ownership, consent, and access control. The integration of privacy-preserving mechanisms such as homomorphic encryption, zero-knowledge proofs, and secure multi-party computation is discussed as a pathway to sustainable data security. The findings emphasize the urgent need for interoperable standards and policy-driven frameworks to protect stakeholders, particularly smallholder farmers, within smart agricultural marketplaces.
Byzantine Fault Tolerance (BFT) protocols are fundamental to achieving consensus in distributed systems where some nodes may behave maliciously. However, traditional BFT mechanisms often rely on strong trust assumptions in a majority of honest participants or incur significant communication overhead for extensive verification, thereby limiting scalability and introducing explicit points of trust. This paper proposes a novel approach to verifiable Byzantine agreement that leverages the power of Zero-Knowledge Proofs (ZKPs) to enhance trustlessness and verifiability. By integrating ZKPs into the consensus process, participants can cryptographically prove the correctness of their protocol actions and proposed states without revealing the underlying sensitive information or requiring every other node to re-execute complex computations. This paradigm shift enables a new class of BFT protocols where agreement is not merely reached but is {em verifiably} correct by any observer, reducing implicit trust and increasing transparency. We outline a conceptual framework for such a ZKP-enhanced BFT protocol, discussing the key integration points for zero-knowledge proofs, the expected benefits in terms of security and scalability, and the challenges associated with its implementation. Our approach aims to pave the way for more robust, scalable, and genuinely trustless decentralized systems.
Shrutika Singh, Anton Alyakin, Daniel Alexander Alber, Jaden Stryker Ā· 12 authors
The performance of Large Language Models (LLMs) on multiple-choice question (MCQ) benchmarks is frequently cited as proof of their medical capabilities. We hypothesized that LLM performance on medical MCQs may in part be illusory and driven by factors beyond medical content knowledge and reasoning capabilities. To assess this, we created a novel benchmark of free-response questions with paired MCQs (FreeMedQA). Using this benchmark, we evaluated three state-of-the-art LLMs (GPT-4o, GPT-3.5, and LLama-3-70B-instruct) and found an average absolute deterioration of 39.43% in performance on free-response questions relative to multiple-choice (p = 1.3 * 10 -5 ) which was greater than the human performance decline of 22.29%. To isolate the role of the MCQ format on performance, we performed a masking study, iteratively masking out parts of the question stem. At 100% masking, the average LLM multiple-choice performance was 6.70% greater than random chance (p = 0.002) with one LLM (GPT-4o) obtaining an accuracy of 37.34%. Notably, for all LLMs the free-response performance was near zero. Our results highlight the shortcomings in medical MCQ benchmarks for overestimating the capabilities of LLMs in medicine, and, broadly, the potential for improving both human and machine assessments using LLM-evaluated free-response questions.
Open access
Artificial Intelligence in Healthcare and Education
Istiaque Ahmed, Tadashi Nakano, Kentaroh Toyoda, Thi Hong Tran
Digital identity verification has become crucial to every service in daily life. The privacy concerns associated with traditional Know Your Customer (KYC) systems have come to the forefront. These systems often require the sharing of personal information, which is stored in centralized databases, making them vulnerable to unauthorized access. To address these challenges, this work implements an electronic KYC system with selective disclosure using Merkle Tree and Zero-Knowledge Proofs (ZKP). Selective disclosure enables users to share only the necessary information, thereby reducing the exposure of sensitive data. ZKP enables the verification of this information without revealing the actual data, ensuring that privacy is preserved. The combination of selective disclosure and zkSNARKs in the proposed framework provides a solution for generating a single proof compared to multiple market proofs. This work demonstrates significant improvements in privacy protection compared to traditional identification systems. The implementation process and performance evaluation explore its potential impact on eKYC.
The competitive hospitality sector faces a growing credibility crisis, where rising consumer skepticism regarding "greenwashing" severely limits the ability of hotels to capture the Sustainable Revenue Premium. This research addresses a critical gap in Sustainable Supply Chain Management (SSCM) literature by empirically modeling the "Credibility Mechanism"āthe process by which digital technology resolves information asymmetry to monetize sustainability claims. Focusing on the complex Food and Beverage (F&B) supply chains of emerging archipelagic economies, the study employs a rigorous sequential mixed-methods design. First, Design Science Research was utilized to architect a permissioned cross-chain blockchain framework integrating Zero-Knowledge Proofs (ZKPs) for verifiable, private provenance. Subsequently, Partial Least Squares-Structural Equation Modeling (PLS-SEM) confirmed that blockchain-enabled transparency significantly mitigates perceived greenwashing risk, which in turn fosters Customer Trust. Critically, the study validates financial outcomes using a Stochastic Frontier Bayesian Model (SFBM) applied to longitudinal hotel data. Results demonstrate that adopting this traceable framework yields an 8.4% increase in F&B revenue efficiency and sustains a 5.1% price premium for ethically sourced items. These findings provide profound theoretical advancements by redefining SCM risk mitigation through Information Governance rather than material redundancy. Managerially, the research offers a data-driven justification for high-tech investment, proving that verifiable transparency is a direct revenue driver essential for competitive advantage in opaque markets.
Vehicle platoon (VP), as a typical form of traffic cooperation, can significantly enhance traffic efficiency and safety in Vehicular Ad hoc Networks (VANETs). However, malicious vehicles in VP poses a severe threat to the security of entire VP, requiring to be efficiently traced by identity authentication. In this paper, we propose a lattice-based efficient and traceable privacy-preserving batch authentication scheme for vehicle platoon in VANETs, named LETA. First, we design a dynamic VP identity structure VPD-Tree which is constructed based on hash tree and pseudonyms of vehicles to preserve privacy. Then, an aggregate signature is constructed based on VPD-tree and modular lattice for secure and efficient batch authentication of VP. Finally, Zero-Knowledge Proofs (ZKP) is applied on the VPD-Tree structure to anonymously and efficiently trace the malicious vehicles of VP. Security analysis shows that LETA achieves stronger security guarantees, thereby offering a more secure solution than existing approaches. Moreover, performance evaluations show that LETA achieves lower computation and communication overheads through the VPD-tree structure and efficient batch authentication scheme.
Smart contracts enable programmatic agreements but face two persistent problems: high on-chain cost (throughput/latency) and weak privacy (public ledger exposes transaction semantics). We propose a hybrid on-chain/off-chain commitment scheme (HOC-C) that combines lightweight on-chain commitments, verifiable off-chain computation, and succinct zero-knowledge proofs to deliver privacy-preserving contract execution at scale. In HOC-C, sensitive inputs and heavy computations are executed off-chain by a consortium of replicated verifiers; the verifiers publish a succinct zk-SNARK proof of correct execution plus a small state commitment on-chain. The on-chain contract verifies the proof and updates state atomically. To prevent malicious collusion among verifiers, HOC-C integrates an economic incentive layer and challenge windows where anyone can publish refutation proofs; the refutation burden is designed to be less than the honest-verifier cost. We implement HOC-C using a prototype that plugs into an EVM-compatible chain (Ethereum testnet) and evaluate performance for representative workloads (private auctions, confidential supply-chain workflows, private token-transfer batching). The system reduces gas cost by an order of magnitude compared to naive on-chain execution while preserving end-to-end confidentiality for user inputs. We analyze security properties (soundness, liveness, and economic incentive compatibility) and discuss trade-offs: proof generation latency vs. throughput, verifier decentralization vs. amortized cost. HOC-C offers a practical roadmap for adopting private, inexpensive smart contracts on mainstream blockchains.
Hikaru Okamoto, Vu Trung Duong Le, Hoai Luan Pham, Van Tinh Nguyen Ā· 5 authors
Zero-Knowledge Proof (ZKP) is a privacy-preserving protocol that allows a prover to demonstrate the validity of a statement without revealing its details. A widely used primitive of ZKP, Zero-Knowledge Succinct Non-Interactive Argument of Knowledge (zk-SNARK), has attracted significant attention in edge computing; however, edge devices face severe resource constraints when processing its computational bottleneck, Montgomery multiplication. This paper proposes MMzk, a lightweight hardware architecture optimized for 384-bit Coarsely Integrated Operand Scanning (CIOS) Montgomery multiplication, which is primarily employed in multi-scalar multiplication (MSM) of zk-SNARKs. To achieve both high performance and resource efficiency, MMzk core operating at the System-on-Chip (SoC) level integrates three key optimizations: resource sharing of two adders, a subtractor, and a multiplier; effective memory organization for large-data processing; and double-buffering memory scheduling. Implementation results on a Xilinx ZCU102 SoC show that MMzk core operates at up to 250MHz with a utilization of 3,590 LUTs, 1,648 FFs, 16 DSPs, and 6 BRAM36 blocks. Compared with existing FPGA-based counterparts, it achieves a throughput of 657.53Mbps (up to 35.9 times higher) and an area efficiency of 81.48Kbps/eLUT (up to 9.5 times higher). Furthermore, real-time evaluation demonstrates an energy efficiency of 9,030Mbps/W at 250MHz and 67mW, surpassing modern CPUs by up to 52.9 times. These results confirm MMzk core as an energy-efficient and high-performance solution for zk-SNARK-based blockchain systems and other Montgomery multiplication applications.
Thanh Hai To, Vu Trung Duong Le, Van Tinh Nguyen, Van-Tuan Luu Ā· 6 authors
Zero-knowledge proofs have become an essential component for providing privacy and verifiability in decentralized systems. Existing techniques, such as zk-SNARKs, have intrinsic constraints, including the necessity for a trusted setup and sensitivity to quantum attacks, which make them unsuitable for high-assurance applications such as digital banking. In this paper, we provide a viable zk-STARK-based verification system that eliminates the trusted setup while maintaining long-term post-quantum security. Our system integrates off-chain proof generation using Cairo 0, on-chain verification through Cairo 2 smart contracts on Starknet, and decentralized proof storage via IPFS and Filecoin. Experimental results show that the time users wait from transaction submission to confirmation is approximately 1.15 seconds on average, with Cairo 2 contract verification completing in 0.73 seconds and consuming a gas cost of 0.0158 STRK (equivalent to approximately 0.00229 USD) per first-time execution. End-to-end latency for proof publication to IPFS and Filecoin is evaluated separately, reaching up to 63 minutes and 41 hours, respectively. However, these steps run asynchronously without impacting user responsiveness. Compared to zk-SNARK and Bulletproof-based systems, our hashbased, transparent architecture is more scalable, auditable, and quantum-resistant. These findings show that it is possible to install real-world, privacy-preserving, post-quantum verification pipelines for next-generation financial systems.
To address the issues of privacy-utility imbalance, insufficient incentives, and lack of verifiable computation in current medical data sharing, this paper proposes a blockchain-based fair verification and adaptive differential privacy mechanism. The mechanism adopts an integrated design that systematically tackles three core challenges: privacy protection, fair incentives, and verifiability. Instead of using a traditional fixed privacy budget allocation, it introduces a reputation-aware adaptive strategy that dynamically adjusts the privacy budget based on the contributorsā historical behavior and data quality, thereby improving aggregation performance under the same privacy constraints. Meanwhile, a fair incentive verification layer is established via smart contracts to quantify and confirm data contributions on-chain, automatically executing reciprocal rewards and mitigating the trust and motivation deficiencies in collaboration. To ensure enforceable privacy guarantees, the mechanism integrates lightweight zero-knowledge proof (zk-SNARK) technology to publicly verify off-chain differential privacy computations, proving correctness without revealing private data and achieving auditable privacy protection. Experimental results on multiple real-world medical datasets demonstrate that the proposed mechanism significantly improves analytical accuracy and fairness in budget allocation compared with baseline approaches, while maintaining controllable system overhead. The innovation lies in the organic integration of adaptive differential privacy, blockchain, fair incentives, and zero-knowledge proofs, establishing a trustworthy, efficient, and fair framework for medical data sharing.
Riku Miyake, Toru NAKANISHI, Teruaki Kitasuka, Zhuotao Lian
Although current digital identity systems are centralized, decentralized systems based on Verifiable Credentials (VCs) are gaining attention and moving towards practical implementation. As one of VC systems, a VC system with selective disclosure has been proposed, where credentials are represented as directed graph based on the concept of Linked Data (LD). However, in the existing VC system, the verification time increases depending on the number of RDF terms that correspond to vertices and edges in the proved graph, due to the characteristics of the utilized signature scheme. Meanwhile, a zero-knowledge proof system for directed graphs using a pairing-based accumulator has been proposed. This system is characterized by its verification time and proof data size being independent of the number of vertices and edges in the graph. In this paper, we propose a LD-based VC system with selective disclosure that leverages the zero-knowledge proof system on graph; the verification time and proof size are independent of the number of vertices and edges. Furthermore, we reduce the proof data size by modifying the signature scheme from AHO signatures to SPS-EQ signatures and from the pairing-based accumulator to a set commitment. We implement and evaluate the proposed system on a PC.
Recent advances in artificial intelligence (AI), particularly deep learning, have led to widespread adoption across various applications. Yet, a fundamental challenge persists: how can we verify the correctness of AI model inference when model owners cannot (or will not) reveal their parameters? These parameters represent enormous training costs and valuable intellectual property, making transparent verification difficult. In this paper, we introduce a zero-knowledge framework capable of verifying deep learning inference without exposing model internal parameters. Built on recursively composed zero-knowledge proofs and requiring no trusted setup, our framework supports both linear and nonlinear neural network layers, including matrix multiplication, normalization, softmax, and SiLU. Leveraging the Fiat-Shamir heuristic, we obtain a succinct non-interactive argument of knowledge (zkSNARK) with constant-size proofs. To demonstrate the practicality of our approach, we translate the DeepSeek model into a fully SNARK-verifiable version named ZK-DeepSeek and show experimentally that our framework delivers both efficiency and flexibility in real-world AI verification workloads.
In the need for high-security mechanisms, Trust Management Systems (TMSs) are implemented in vehicular networks such as Vehicular Ad Hoc Networks (VANETs) and the Internet of Vehicles (IoV) to ensure reliable interactions between vehicles. These systems nowadays are a key factor in building up security by evaluating and managing trust relationships among network participants. However, TMSs are inherently very vulnerable to Trust Manipulation Attacks (TMA), where we find malicious nodes attempting to deceive trust models by exploiting their evaluation mechanisms. One critical variant of this attack involves malicious nodes creating multiple fake identities, known as the Sybil attack, to falsely reinforce their trustworthiness. This deception will totally mislead legitimate vehicles, manipulate the decision-making processes, and at the end compromise the overall security and reliability of the network. To address this challenge, we propose a Zero-Knowledge Proof (ZKP)-based trust authentication scheme that ensures each vehicle can prove its legitimacy without exposing sensitive information. Our approach can and will prevent attackers from fabricating multiple identities to manipulate trust values. By integrating cryptographic authentication with trust management, our method significantly strengthens security and ensures that only legitimate vehicles can participate in trust-based evaluations. Through simulations, we demonstrate the effectiveness of our proposed solution in reducing the risk of Sybil-based. The results indicate that our approach not only enhances security but also maintains efficient trust computation, making it a viable solution for real-world vehicular networks.
Neeraj Purushotham, Mallepula Likhitha, Y Sai Pragathi, C RaviKiran Ā· 5 authors
Secure and verifiable cloud storage auditing is a significant issue of concern with identity-based systems, especially against malicious auditors and forged proof attacks. In this paper, the enhanced identity-based public audit protocol has been enhanced with blockchain-supported federated trust and quantum resilient cryptographic priming. The suggested Quantum-Resistant Federated Identity-Based Auditing (QFIBA) scheme does not have a single-point dependency on the PKG and proposes a lattice-based identity encryption that ensures post-quantum security. The proposed system implements privacy-preserving audits via zero-knowledge verification made using zk-SNARK without exposing user metadata. The results of experiments carried out on the JPBC and CRYSTALS-Dilithium schemes show that the tag generation cost is reduced by 19.6 per cent, the proof generation overhead is reduced by 14.2 per cent and the verification rate is 12.8 times faster than the better IBPA scheme with the same audit soundness and forgery resistance. Security guarantees: It has been shown that QFIBA is secure to the CDH and LWE assumptions. Such findings confirm the practical effectiveness of the scheme and its resilience to the future cloud ecosystems where federated trust and quantum resilience is needed.
Federated Learning (FL) has become a promising method for training machine learning models while protecting patient privacy. This systematic review examines the use of privacy-preserving techniques in FL within decentralized healthcare systems. It compares existing methods such as Differential Privacy (DP), Trusted Execution Environment (TEE), Zero Knowledge Proofs (ZKP), Homomorphic Encryption (HE), Watermarking, Blockchain, and Secure Multi-Party Computation (SMPC) based on regulatory compliance, scalability, computational cost, complexity, and mathematical foundations. The principle challenges in decentralized healthcare like heterogeneous data, privacy risks, security threats, and compliance issues have been discussed. The review also highlights the importance of adhering to global regulations like HIPAA, GDPR, and country-specific data protection laws. Furthermore, it discusses open challenges and suggests future research directions to overcome current limitations, including computational efficiency, adversarial attacks, and the creation of policy frameworks for standardization. Overall, this review provides a unique perspective on ethical, secure, and scalable privacy-preserving FL models for the next generation of healthcare applications. ā¢ Analyzes essential techniques: Differential Privacy, SMPC, HE, TEE, ZKP, and Blockchain. ā¢ Reviews key privacy techniques: DP, SMPC, HE, TEE, ZKP, and Blockchain. ā¢ Compares methods based on cost, scalability, and resilience in FL. ā¢ Identifies issues such as non-IID data, high communication, and compliance. ā¢ Suggests hybrid and hardware-aided frameworks for secure FL. ā¢ presents future needs in terms of explainability, interoperability, and quantum security.