In today's digital age, cloud storage and computing have become indispensable. Resource-constrained clients such as individuals and small organisations increasingly rely on powerful servers to store, manage and process their data. However, outsourcing data to external servers leads to significant privacy concerns, particularly when dealing with sensitive information such as medical records, financial transactions, or personal data. Fully homomorphic encryption (FHE) is a cryptographic technique that allows computation over encrypted data. In secure outsourcing with FHE, a client sends encrypted data to a server, which can perform requested computations without accessing the original data. The server returns the resulting ciphertexts, which the client can decrypt to obtain the final output. Despite its strong privacy guarantees, the practical adoption of FHE is limited by two main challenges: efficiency, which arises from the substantial performance overhead of FHE; and integrity, which stems from the lack of mechanisms to verify the correctness of the outsourced computation. In this thesis, we contribute to addressing these challenges in three aspects. First, we optimise oblivious algorithms for use in FHE, achieving improvements in key performance metrics and accelerating both bootstrapping and a range of applications. Second, we build efficient privacy-preserving information systems based on FHE. These include (i) two private machine learning protocols, the k-nearest neighbour algorithm and decision tree evaluation, (ii) SQUID, a secure system for storing and analysing genotype-phenotype data, and (iii) a protocol for securely delegating zero-knowledge proof generation. Third, we construct verifiable secure delegation of computation through FHE techniques. We provide the notion of blind proofs to provide integrity guarantees and demonstrate its practicality using blind zkSNARKs, a concrete instantiation of blind proofs.
P. Prakash, Faheema Kattakath Sanil, Jeffrey Tom Shaji, Saravanan Palani · 5 authors
The adoption of privacy-preserving techniques in healthcare is significant, especially while handling sensitive medical information. Traditional machine learning approaches raise significant concern regarding privacy, regulations, and data protection. Federated learning has emerged as an effective machine learning approach that enables a group of local models to collaboratively train the global model by sharing their updates instead of sharing the sensitive medical data. Nevertheless, a significant issue with federated learning is its vulnerability to various attacks, including model corruption and data tampering. The authors propose a methodology for developing a secure and privacy-safeguarded collaborative learning model by integrating zero knowledge proof (ZKP) with federated learning (FL). The proposed RS-ZKP methodology utilizes Pedersen commitments within ZKP to verify feature importance, ensuring that they fall within specified bounds without disclosing the actual values. The methodology is validated on two benchmark datasets using metrics accuracy, precision, recall, and F1 score.
With the rapid development of quantum computing technology, traditional encryption methods face severe security threats in multi-party privacy intersection protocols in federated learning. In this paper, we propose a new protocol based on post-quantum cryptography. Firstly, lattice-based homomorphic encryption and zero-knowledge proof technology are used to achieve key generation and parameter initialization against quantum attacks. Secondly, ciphertext data encoding is carried out to support homomorphic operations. Next, a zero-knowledge proof is used to verify the correctness of the ciphertext intersection calculation. Finally, the protocol is embedded into the federated learning workflow, adaptively adjusting the parameters. Experimental results show that the protocol achieves the NIST (National Institute of Standards and Technology) security level 3, and the privacy leakage rate is less than 1.2%, the communication and computational costs are controllable, and the protocol does not bring great influence to the accuracy of the federated learning model. The experimental results verify that the protocol can provide a reliable protection for the privacy of federated learning data in the quantum era.
Certificate authentication in online systems is required to ensure integrity and authenticity and prevent forgery. Traditional blockchain-based approaches work with double-chain architecture without any privacy-preservation capability or pack whole certificates into a single chain and incur substantial storage overhead. In this study, we introduce a light-weight dual-blockchain architecture with an external verification and audit side-chain and an inner chain for offline storing certificates. Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (Zero Knowledge Proofs) are employed to sign hashes without revealing sensitive information, and threshold signatures are employed to sign certificates. Compared to the traditional single-chain and double-chain architecture, the proposed system realizes up to 30% lower latency and 25% higher throughput based on experimental results on 1,000–100,000 certificate dataset. These results indicate the efficiency, scalability, and privacy-preserving feature of the proposed solution, which can be applied to large-scale applications for certificate management.
Jiaxi Liu, Lin Sun, Tianyu Kang, Di Wu · 7 authors
Federated Learning (FL) enables model training on distributed devices while preserving data privacy. However, malicious clients can submit fabricated model updates to fraudulently obtain training rewards, a behavior known as free-rider attacks. Existing detection-based solutions analyze anomalies in model updates but lack direct evidence of local training, making it fail to fully prevent free-riders. To address this limitation, we propose zkVFL, a verifiable FL framework leveraging Zero-Knowledge Proofs (ZKP) to ensure the integrity of local training while preserving privacy. To reduce the computational overhead of proof generation in ZKP, zkVFL introduces two novel techniques: (i) anomaly-aware client sampling to selectively perform ZKP verification and (ii) A recursive ZKP protocol (ReMPoT), incorporating a pruning-based layer selection technique, reduces proof generation costs. Experimental results demonstrate that zkVFL improves the accuracy and convergence of FL training under free-rider attacks while significantly reducing the computational and memory overhead of proof generation on resource-constrained devices.
Denis Wapukha Walumbe, Gabriel Kamau, Jane Wanjiru Njuki
With the rising integration of blockchain in critical domains such as healthcare, designing efficient, lightweight, and privacy-preserving consensus mechanisms remain a significant challenge.Existing Proof-of-Stake (PoS) implementations often incur high computational and communication overhead, making them unsuitable for telemedicine systems.This study proposed LightweightPoS, a novel voting mechanism designed for this environment.The proposed mechanism incorporates a cluster-based voting to minimize message complexity, Byzantine Agreement protocol for robust fault tolerance and cryptographic sortition to ensure fairness and privacy.This implementation slashes global communication, reducing message complexity by over 95% compared to traditional PoS models.The study evaluated the proposed and baseline mechanisms through simulations using real-time telemedicine data sensors.The results demonstrated that the proposed mechanism consistently achieved sub-10ms latency, high transaction throughput (up to 2400 TPS) and low energy consumption (~0.002kWh per round).It significantly outperformed baseline mechanism like Algorand and Ouroboros.Furthermore, the system included an effective Byzantine node detection, ensuring reliability under adversarial conditions.This work contributes a practical consensus voting mechanism that balances privacy and regulatory compliance.It provides a robust foundation for deploying blockchain technology in privacy-sensitive telemedicine applications.
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 protocol’s cryptographic steps. However, this security assumption is confined to the protocol’s 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 function’s 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.
Hessah A. Alsalamah, Saeed Alqahtani, Ghazlan Al-Arifi, Jana Al-Sadhan · 8 authors
Assisted Reproductive Technology (ART), particularly In Vitro Fertilization (IVF), generates highly sensitive medical data classified as Protected Health Information (PHI) under international privacy and data protection laws. Ensuring the secure, transparent, and ethically governed management of this data is both essential and legally mandated. However, conventional Electronic Medical Record (EMR) systems often present significant challenges, including data-integrity risks, unauthorized access, and limited patient control—issues that become especially critical in contexts such as fertility preservation for cancer patients. EmbryoTrust introduces a blockchain-based framework designed to ensure the confidentiality, integrity, and availability of IVF-related information through a private, permissioned network integrated with role-based access control (RBAC). Smart contracts, implemented in Solidity on the Ethereum platform, verify spousal identities and enforce data immutability in compliance with religious legislation and ethical regulations. Off-chain data are stored in MongoDB for scalable, privacy-preserving management, while on-chain summaries provide tamper-evident traceability and verifiable auditability. The system was deployed and validated on the Ethereum Holešky testnet using Solidity 0.8.21 and Node.js 18.17, achieving an average transaction-confirmation time of 2.8 s, 99.9% uptime and a 95% user-satisfaction rate. Functional, integration, and usability testing confirmed secure and efficient data handling with minimal computational overhead. Comparative analysis demonstrated that the hybrid on-/off-chain architecture reduces latency and gas costs while maintaining automated compliance enforcement. The modular design enables adaptation to other jurisdictions by reconfiguring ethical and regulatory parameters within the smart-contract layer, ensuring flexibility for global deployment. Overall, the EmbryoTrust framework illustrates how blockchain logic can technically enforce medical and ethical rules in real time, providing a reproducible model for secure, culturally compliant, and privacy-preserving digital-health information management. Its alignment with Saudi Vision 2030 and the Wold Health Organization (WHO) Global Strategy on Digital Health 2020–2025 highlights its potential as a scalable solution for next-generation ART information systems.
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.
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.
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 · 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 · 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 contributors’ 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.
Neeraj Purushotham, Mallepula Likhitha, Y Sai Pragathi, C RaviKiran · 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.
Pedro F. F. Abreu, Maria R. F. M. Ferreira, Luis H. O. Mendes, Geraldo A. Sarmento Neto · 8 authors
The proliferation of Internet of Things (IoT) devices necessitates secure, scalable, and cost-effective access control mechanisms. While blockchain and Non-Fungible Tokens (NFTs) offer a decentralized paradigm for managing permissions, they remain vulnerable to off-chain resource exhaustion attacks and present practical implementation challenges for low-cost devices. This paper proposes a novel hybrid architecture that enhances NFT-based access control with an off-chain gateway acting as both a Smart Reputation System (SRS) and a delegated signer. This hybrid model combines fast, off-chain pre-validation with authoritative on-chain verification. The SRS serves as a security firewall, mitigating high-frequency invalid requests by dynamically managing the reputation of each device and imposing temporary bans on malicious actors. By delegating cryptographic signing to the gateway, low-cost IoT devices are absolved of managing private keys, significantly reducing their complexity and cost. An experimental evaluation of the implemented system was conducted to assess its resilience against Denial-of-Service attacks. The findings indicate that the system successfully neutralizes threats in under 3 seconds. During this process, a stable end-to-end latency of approximately 626 ms is maintained for legitimate users, with the gateway’s reputation logic introducing a negligible performance overhead of less than 1%. This hybrid approach proves to be a practical and effective solution for deploying secure and resilient access control in real-world IoT environments.
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. • Analyzes essential techniques: Differential Privacy, SMPC, HE, TEE, ZKP, and Blockchain. • Reviews key privacy techniques: DP, SMPC, HE, TEE, ZKP, and Blockchain. • Compares methods based on cost, scalability, and resilience in FL. • Identifies issues such as non-IID data, high communication, and compliance. • Suggests hybrid and hardware-aided frameworks for secure FL. • presents future needs in terms of explainability, interoperability, and quantum security.
One-Time Passwords (OTPs) are a core component of multi-factor authentication in banking, e-commerce, and digital platforms. However, conventional delivery channels such as SMS and email are increasingly vulnerable to SIM-swap fraud, phishing, spoofing, and session hijacking. This study proposes an end-to-end mobile authentication architecture that integrates a permissioned Hyperledger Fabric blockchain for tamper-evident identity management, an AI-driven risk engine for behavioral and SIM-swap anomaly detection, Zero-Knowledge Proofs (ZKPs) for privacy-preserving verification, and geolocation-bound OTP validation for contextual assurance. Hyperledger Fabric is selected for its permissioned governance, configurable endorsement policies, and deterministic chaincode execution, which together support regulatory compliance and high throughput without the overhead of cryptocurrency. The system is implemented as a set of modular microservices that combine encrypted off-chain storage with on-chain hash references and smart-contract–enforced policies for geofencing and privacy protection. Experimental results show sub-0.5 s total verification latency (including ZKP overhead), approximately 850 transactions per second throughput under an OR-endorsement policy, and an F1-score of 0.88 for SIM-swap detection. Collectively, these findings demonstrate a scalable, privacy-centric, and interoperable solution that strengthens OTP-based authentication while preserving user confidentiality, operational transparency, and regulatory compliance across mobile network operators.