Recent advancements in cloud computing, data privacy, and cryptography have sparked a growing interest in Verifiable Computation (VC) in both industry and academia. In particular, zero-knowledge proof (ZKP) algorithms are gaining rapid traction due to their strong privacy guarantees. However, they are notoriously computationally intensive, making performance a critical concern. Given the inherent data parallelism and heavy use of vector operations in ZKP computations, multicore CPUs and GPUs offer a promising acceleration path. Unfortunately, accelerated programming for ZKP remains challenging: ZKP algorithms evolve rapidly, their structures grow increasingly complex, and writing high-performance ZKP code is tedious, error-prone, non-portable, and unfriendly to algorithm developers. We present an end-to-end compiler framework, Zera, that lowers ZKP algorithms to parallel hardware for efficient acceleration, with minimal programmer effort. By effectively leveraging ZKP algorithm patterns and trends, we are able to automate the key performance optimizations, with a succinct linguistic extension and a set of practical compiler customizations. Consequently, with just 92 lines of trivial high-level annotation added to the original 7,000 lines of C++ code, our single-source code solution delivers 33.9× and 24.0× speedup on GPU over a highly optimized serial C++ implementation on CPU and an existing multithreaded Rust baseline on CPU, respectively. Compared to our hand-optimized GPU/CUDA implementation requiring an extra 2,000 lines of low-level code (roughly 60 programmer hours), our compiler-generated GPU implementation is only 58% slower (1.58× slowdown) on large inputs, demonstrating a compelling trade-off between performance and productivity.
Mohammed Ibraheem Hussein, Ohood Saadoon Hlail, Asma Ibrahim Hussein, Amjed Abbas Ahmed · 6 authors
Balancing efficient threat detection with data privacy becomes increasingly difficult as cyber threats develop in complexity. The Adaptive Zero-Knowledge Threat Hunting Framework (AZTH), a revolutionary integration of zero-knowledge proofs (ZKP) and artificial intelligence (AI) for private and secure cybersecurity operations, is presented in the presented study. AZTH maintains strong confidentiality regarding sensitive data yet uses federated learning, quantum-resistant cryptography, and dynamic deception systems to improve threat intelligence sharing as well as real-time threat mitigation. Together with an assessment of its efficacy in several operating situations, the architecture, approach, and possible uses of the framework are given.
This conference paper aims at understanding how blockchain technology can be utilized in financial auditing in relation to smart contract, consensus, and privacy-preserving technology. The paper focuses on how Deloitte and Santander Bank incorporate blockchain for auditing, especially filing compliance verification, data integrity, and privacy concerns, by employing a qualitative, multiple-case research study. Proof of consensus such as Proof of Authority (PoA) and the Practical Byzantine Fault Tolerance (PBFT) guarantee that the records of the blocks are unaltered since they involve a consensus on the validity of a particular transaction in a distributed fashion. Furthermore, solutions like Zero-Knowledge Proofs (ZKPs), and homomorphic encryption enable the auditors to confirm transaction legitimacy without compromising the customers' data privacy laws. Research evidence suggests that blockchain has the potential to improve efficiency, integrity, and security rate in financial audit but major issues that hinder its widespread application include high cost, lack of acceptance by authorities, and technical issues. The paper provides solid suggestions regarding the adoption of blockchain in auditing and provides suggestions for the future research to explore more about the efficiencies of the audit that can be obtained with the implementation of blockchain and the regulatory policies that could make blockchain more suitable for the various industries.
To solve the privacy leakage and identity island problems in cross-chain interaction, we propose an anti-quantum cross-chain identity authentication approach based on dynamic group signature (DGS-AQCCIDAA) for smart education. The relay-based cross-chain model promotes interconnection in heterogeneous consortium blockchains. DGS is used as the endorsement strategy for cross-chain identity authentication. Our approach can ensure quantum security under the learning with error (LWE) and inhomogeneous small integer solution (ISIS) assumptions, and it uses non-interactive zero-knowledge proof (NIZKP) to protect user identity privacy. Our scheme has low calculation overhead and provides anonymous cross-chain identity authentication in the smart education system.
This article explores the complex intersection of technological innovation and privacy considerations in cloud identity management systems. It traces the evolution from traditional authentication methods to sophisticated cloud-based frameworks that now incorporate adaptive authentication, federation protocols, biometric verification, and behavioral analytics. While these advances enhance security and user experience, they simultaneously introduce profound privacy challenges regarding data collection scope, user profiling, and cross-platform tracking. The article examines how regulatory frameworks, industry self-regulation, and stakeholder perspectives shape the governance landscape of digital identity. Drawing on interdisciplinary research, the article reveals how trust formation in digital environments correlates with transparency practices and how trust erosion carries consequences beyond immediate user relationships. Looking forward, emerging technologies like decentralized identity and zero-knowledge proofs offer promising privacy-preserving alternatives, while evolving market dynamics and user expectations create both challenges and opportunities. The article concludes with actionable recommendations for designing and implementing identity systems that achieve an optimal balance between robust security, operational efficiency, and respect for individual privacy rights—a critical imperative for sustainable digital ecosystems in increasingly connected societies.
The rapid growth of digital technologies has intensified concerns about data privacy and security. Blockchain technology, combined with advanced cryptographic methods, presents a promising solution to enhance digital privacy by enabling decentralized, transparent, and tamper-resistant systems. This article explores the foundational principles of blockchain and cryptography, evaluates their interplay in preserving digital privacy, and examines current challenges and future directions. Through comprehensive analysis, the study highlights how cryptographic protocols such as zero-knowledge proofs and homomorphic encryption can significantly augment privacy on blockchain platforms, while addressing inherent scalability and usability challenges.
Although differential privacy (DP) is widely regarded as the de facto standard for data privacy, its implementation remains vulnerable to unfaithful execution by servers, particularly in distributed settings. In such cases, servers may sample noise from incorrect distributions or generate correlated noise while appearing to follow established protocols. This work addresses these malicious behaviours in a distributed client-server-verifier setup, under Verifiable Distributed Differential Privacy (VDDP), a novel framework for the verifiable execution of distributed DP mechanisms. We systematically capture end-to-end security and privacy guarantees against potentially colluding adversarial behaviours of clients, servers, and verifiers by characterizing the connections and distinctions between VDDP and zero-knowledge proofs (ZKPs). We develop three novel and efficient instantiations of VDDP: (1) the Verifiable Distributed Discrete Laplace Mechanism (VDDLM), which achieves up to a 400,000x improvement in proof generation efficiency with only 0.1--0.2x error compared with the previous state-of-the-art verifiable differentially private mechanism and includes a tight privacy analysis that accounts for all additional privacy losses due to numerical imprecisions, applicable to other secure computation protocols for DP mechanisms based on cryptography; (2) the Verifiable Distributed Discrete Gaussian Mechanism (VDDGM), an extension of VDDLM that incurs limited overhead in real-world applications; and (3) an improved solution to Verifiable Randomized Response (VRR) under local DP, as a special case of VDDP, achieving up to a 5,000x reduction in communication costs and verifier overhead.
Don Roosan, Rubayat Khan, Saif Nirzhor, Fahmida Hai
Objective: The authors propose and evaluate a novel cybersecurity architecture for telehealth that is resilient against future quantum computing cyber threats. By integrating post-quantum cryptography (PQC) with quantum key distribution (QKD) and privacy-preserving mechanisms, data confidentiality and immutability for patient records in a post-quantum era are ensured. Methods: A multi-layered design approach was adopted. The PQC algorithms (e.g. CRYSTALS-Dilithium) were integrated at the blockchain consensus layer to resist quantum attacks. A directed acyclic graph (DAG)-based ledger managed high transaction throughput and latency constraints typical of telehealth. A QKD-enhanced key management protocol leveraged quantum channels for secure exchanges. Zero-knowledge proofs (ZKPs) and secure multiparty computation (MPC) verified transactions without exposing sensitive patient data. A granular access control model used attribute-based encryption and smart contracts to govern which participants could view or modify encrypted medical records. Results: The prototype was developed within a simulated telehealth network comprising hospitals, clinics, and patient devices. The PQC signatures at the consensus layer provided effective resistance to both classical and anticipated quantum attacks. The QKD facilitated secure key distribution, while ZKPs and MPC enabled validation of healthcare transactions without compromising patient privacy. Despite increased computational overhead, the DAG approach efficiently handled parallel transactions, indicating improved scalability compared to traditional linear blockchains. Conclusion: A QKD-enhanced, PQC-driven framework successfully addresses critical security and privacy requirements, safeguarding medical data from emerging quantum threats. Although overhead and infrastructural costs are significant, sustained cryptographic resilience and robust patient confidentiality underscore its suitability for next-generation healthcare systems. Future studies should explore additional optimizations, homomorphic encryption, and larger-scale pilots under regulatory standards.
Saad Alahmari, Amal Alshardan, Fahd N. Al‐Wesabi, Shaymaa E. Sorour · 8 authors
As healthcare services have become increasingly digitized, Electronic Health Records (EHRs) have become widely adopted, providing seamless data exchange among providers. Conventional EHRs, however, are extremely vulnerable to cyber threats because patients' sensitive data is centralized and transmitted electronically. The paper proposes a decentralized, privacy-preserving framework for managing EHRs on blockchains in order to address these security and privacy concerns. Using cryptographic techniques, such as homomorphic encryption and zero-knowledge proofs, the proposed system enhances security and ensures data integrity. Additionally, the model facilitates scalable, efficient, and secure access to patient records through the integration of cloud-based storage and blockchain. Using smart contracts, we also ensure compliance with healthcare regulations by regulating access control and authentication. As a result of performance evaluations, the proposed approach is demonstrated to be feasible, and the advantages it offers in terms of security, privacy, and efficiency are highlighted.
Implications of blockchain-integrated health data management in metaverse environments in India, where digital transformation is happening at a rapid pace and a unique regulatory and socio-cultural landscape exists, require special focus. This research explores the subtleties of privacy, ownership ownership , and consent in blockchain-enabled health data frameworks in the context of emerging India and addresses the operational and ethical issues of finding a balance between decentralized data structures, India’s regulatory expectations, and the multitude of needs of its population, providing a holistic view of policy and legal considerations for this domain. In India, the IT Act, 2000, and the recently enacted DPDPA 2023 are the laws that exist to deal with privacy concerns, but such laws were built for centralized applications and do not apply to decentralized and immutable blockchain structures. This paper explores how blockchain’s features, including self-sovereign identity and zero-knowledge proofs, can be leveraged toward align with Indian privacy norms, potentially allowing users to retain ownership of their health data while adhering to legal obligations. Data ownership in traditional healthcare systems is ambiguous; who owns the data varies from patient to provider, from provider to provider, and from provider to digital platform. Blockchain technology offers the opportunity for self-sovereign ownership. This paper studies the consequences of decentralized ownership by analyzing how the Indian legal framework may need to be reformed to reconcile user autonomy with public health interests in a decentralized ownership environment. It examines these risks, discusses ethical considerations, and explores policy approaches that could be taken to ensure informed consent informed consent is truly respected and risks are addressed in the virtual health space.
Thomas Miller, Xavier Boyen, Shoufeng Cao, Marcus Foth · 5 authors
Distributed Ledger Technology (DLT) has gained significant attention due to its potential for enabling secure and decentralised systems. However, this premise can only be achieved if information asymmetry is eliminated and there is equal and simultaneous access to consistent and transparent data by all participants in the network. In this article, we explore various DLT information symmetry challenges, including theoretical fundamentals and practical hurdles in relation to the technology stack. We explore the potential of using DLT to facilitate mutual knowledge sharing in E-Commerce scenarios via Proofs of Information Symmetry (PoIS). We found that PoIS have the ability to significantly improve trust between transacting parties, specifically in the context of producer-consumer relationships and associated investor-asset management and contract obligations. In addition, we discovered that the effectiveness of PoIS strongly depends on the presence of a reliable notification system. Our study found that the utilisation of Zero-Knowledge (ZK) proofs can improve confidence among transaction partners, particularly in situations where safeguarding trade secrets or maintaining the privacy of agents is necessary. Our results are demonstrated by deriving from testing PoIS in a commercial context that includes a vehicle under a short-term lease management setup. This evaluation demonstrates the benefit of a contextual ZK proof that maintains the individual privacy of trading partners.
Economic and Technological Systems Analysis
Advanced Research in Systems and Signal Processing
Julio López Fenner, Carlos Castillo-Muñoz, Francisco Escobar, Ana Bustamante-Mora · 5 authors
Privacy-preserving secure multi-party computation protocols are known to face scalability and efficiency challenges in environments where participants hold distinct attributes of the same records (vertical partitioning) or controls a subset of complete records (horizontal partitioning), as in cross-institutional health data analysis or federated IoT analytics, mostly because of communication overhead and the need to address adaptability to large scale or heterogeneous settings. This work introduces a novel MPC protocol based on the Damgård–Jurik cryptosystem and Schnorr zero-knowledge proofs (ZKP), designed to securely aggregate private data distributed across a number of parties. By combining homomorphic encryption with non-interactive ZKP’s, the protocol ensures privacy, correctness, and scalability, aligning with the principles of privacy-enhancing technologies (PETs). Our approach minimizes data exposure, allowing participants to audit results, and achieves linear O(N) communication complexity, thus making it suitable for large-scale applications in secure data analytics and collaborative computing.
Tarek Galal, Valeria Tisch, Katja Assaf, Andreas Polze
Railways provide a critical service and operate under strict regulatory frameworks for implementing changes or upgrades. Despite their impact on the public, these frameworks do not define means or mechanisms for transparency towards the public, leading to reduced trust and complex tracking processes. We analyse the German guideline for railway-infrastructural modifications from proposal to approval, using the guideline as a motivating example for modelling decisions in processes using digital signatures and zero-knowledge proofs. Therein, a verifier can verify that a process was executed correctly by the involved parties and according to specification without learning confidential information such as trade secrets or identities of the participants. We validate our system by applying it to the railway process, demonstrating how it realises various rules, and we evaluate its scalability with increased process complexities. Our solution is not railway-specific but also applicable to other contexts, helping leverage zero-knowledge proofs for public transparency and trust.
Yanghe Pan, Zhou Su, Yuntao Wang, Han Liu · 6 authors
Federated learning (FL) model marketplaces require qualified workers to collaboratively train customized models. However, recruiting optimal workers on a limited budget in non-independent and identically distributed (non-IID) data settings remains a fundamental issue. Moreover, inadequate quality verification exposes the marketplace to spoofing and poisoning attacks, while verifying data and model quality without accessing local storage remains a significant dilemma. To bridge the research gap, this paper proposes a knowledge-aware model customization scheme in FL model marketplaces, to facilitate zero-trust worker recruitment and verification while ensuring privacy preservation. Specifically, (i) we design a knowledge-aware quality evaluation mechanism by leveraging the knowledge of workers, i.e., soft-label predictions of their local models on a privacy-free reference dataset (provided by the customer), to assess their data quality in a privacy-preserving manner. (ii) We formulate the optimal worker recruitment problem under budget constraints as an NP-hard integer programming problem and design a dynamic programming-based optimal worker recruitment algorithm with budget feasibility and computational efficiency. (iii) We devise a two-stage zero-trust quality verification mechanism by utilizing zero-knowledge proof (ZKP) to exclude distrustful workers, thereby preventing spoofing and poisoning attacks. Extensive experimental results demonstrate that the proposed scheme enhances model customization performance by up to 34.3% on label-skewed non-IID data and 36.2% on feature-skewed non-IID data compared with existing representatives.
Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and (potentially sensitive or private) training data. So-called Zero-knowledge Succinct Non-Interactive Arguments of Knowledge (ZK-SNARKs) would appear to provide the capability to verify model inference without access to such sensitive data. However, applying ZK-SNARKs to modern neural networks, such as transformers and large vision models, introduces significant computational overhead. We present TeleSparse, a ZK-friendly post-processing mechanisms to produce practical solutions to this problem. TeleSparse tackles two fundamental challenges inherent in applying ZK-SNARKs to modern neural networks: (1) Reducing circuit constraints: Over-parameterized models result in numerous constraints for ZK-SNARK verification, driving up memory and proof generation costs. We address this by applying sparsification to neural network models, enhancing proof efficiency without compromising accuracy or security. (2) Minimizing the size of lookup tables required for non-linear functions, by optimizing activation ranges through neural teleportation, a novel adaptation for narrowing activation functions' range. TeleSparse reduces prover memory usage by 67% and proof generation time by 46% on the same model, with an accuracy trade-off of approximately 1%. We implement our framework using the Halo2 proving system and demonstrate its effectiveness across multiple architectures (Vision-transformer, ResNet, MobileNet) and datasets (ImageNet,CIFAR-10,CIFAR-100). This work opens new directions for ZK-friendly model design, moving toward scalable, resource-efficient verifiable deep learning.
The integration of machine learning (ML) in healthcare has unlocked transformative potential in disease prediction, personalized treatment, medical imaging, remote patient monitoring, and genomic data analysis. However, the sensitive nature of medical data introduces critical concerns regarding patient privacy, data security, and regulatory compliance. This chapter presents a comprehensive overview of privacy-preserving machine learning approaches tailored for healthcare applications, with a focus on technical frameworks, real-time implementations, and regulatory alignment. It explores the use of advanced techniques such as federated learning, differential privacy, homomorphic encryption, and zero-knowledge proofs to safeguard patient information while maintaining model utility. The chapter also addresses domain-specific challenges in processing real-time health data streams and implementing privacy-aware algorithms in resource-constrained environments. By bridging the gap between technical innovation and clinical applicability, this work emphasizes the importance of secure, scalable, and ethically aligned ML solutions in modern healthcare ecosystems. The discussion was contextualized within current legal frameworks and highlights future directions for research and implementation to ensure trust, transparency, and resilience in data-driven medical systems.
Circom is a popular programming language for writing arithmetic circuits that can be used to generate zero-knowledge proofs (ZKPs) like zk-SNARKS. ZKPs have received tremendous attention in protocols like zkRollups. The Circom circuits are compiled to Rank-1 Constraint Systems (R1CS) circuits, based on which zk-SNARK proofs are generated. However, one major challenge associated with R1CS circuits is the problem of under-constrained circuits, which are susceptible to allowing incorrect computations to pass verification due to insufficient constraints, potentially leading to security vulnerabilities. In this paper, we propose a novel framework CONSCS to automatically verify Circom circuits. Our contributions are threefold: 1) we propose novel circuit inference rules to help reduce the size of circuits and to extract more comprehensive information than existing works; 2) we introduce the novel Binary Property Graph (BPG) as a highly efficient reasoning engine, outperforming all existing tools in effectiveness and efficiency; 3) we leverage fine-grained domain-specific information to guide the SMT solving to address non-linear constraints, increasing the success rate of SMT queries of existing works from 2.68% to 48.84%. We conduct experiments to show that CONSCS enhances the solved rate of existing works from around 50-60% to above 80%.
With the advancement of intelligent power systems worldwide, the transition from traditional power grids to smart grids has significantly transformed power management. Smart grids rely on the real-time collection and aggregation of massive power data, where the aggregated results play a crucial role in load forecasting, equipment status monitoring, and fault detection. However, due to the privacy-sensitive nature of power data, there is a risk of privacy leakage during the aggregation process. While some studies have focused on ensuring privacy protection in data aggregation, challenges such as data consistency and validity verification remain unresolved. To address these issues, this paper proposes a privacy-preserving power grid data aggregation scheme based on blockchain and homomorphic encryption. The proposed scheme ensures privacy protection during data aggregation using homomorphic encryption, guarantees data consistency through blockchain and the Pedersen commitment mechanism, and verifies data validity by integrating blockchain with zero-knowledge proof techniques. Finally, theoretical analysis and experimental results demonstrate the feasibility and practicality of the proposed scheme.
Account-based blockchain anonymous systems use non-interactive zero-knowledge proof schemes to protect user privacy but suffer from limited functionality and vulnerability to security attacks. This paper addresses these issues by proposing an optimized scheme that improves multi-signature authentication and enhances system security. We designed and implemented the first multi-signature authentication mechanism for account-based blockchain anonymous systems, supporting secure zero-knowledge operations such as deposits, withdrawals, transfers, and payments. Additionally, we enhanced security by upgrading the zk-SNARK proof scheme to SE-SNARK, significantly increasing the system’s resistance to attacks. Experimental results show that our approach improves security, flexibility, and overall system practicality without compromising efficiency.
Advanced Steganography and Watermarking Techniques
Blockchain has evolved from supporting cryptocurrencies to transforming industries like finance, healthcare, and supply chain management. Emerging trends focus on scalability with Layer 2 solutions, sharding, and cross-chain interoperability. Sustainability efforts include transitioning to Proof-of-Stake, carbon-neutral blockchains, and renewable energy. AI integration enables decentralized models, secure data sharing, and AI-driven smart contracts. Governments explore CBDCs, while privacy technologies like Zero-Knowledge Proofs enhance security. Challenges remain in regulation, security risks, and adoption, but ongoing innovations are driving blockchain's widespread acceptance.
As digital evidence increasingly growing in significance in healthcare forensics, safeguarding sensitive medical data's confidentiality, integrity, and limited access remains to be an important issue. Existing forensic evidence management systems are subject to data breaches and illegal access since they frequently lack significant privacy-preserving measures. In order to overcome such challenges, this research suggests a Blockchain-Based Custody Evidence Management System for Healthcare Forensics, which combines blockchain technology, machine learning, and encryption methods to improve security, privacy, and accessibility. To ensure accurate and efficient gathering of information, machine learning algorithms are used to extract handwritten and printed text from medical photographs. AES encryption ensures safe storage, while Fully Homomorphic Encryption (FHE) is used for dynamic access level control to protect gathered evidence. Identity verification is made possible via a web-based authentication system that uses Zero-Knowledge Proofs (ZKP) to protect privacy by preventing the disclosure of personal data. By preventing unintended modifications, blockchain technology is used to preserve the custody chain's integrity. Furthermore, machine learning-driven PII detection and masking methods balance the requirement for forensic investigation with privacy compliance by controlling data accessibility according to access entitlements. Based on permitted access levels, the system makes it possible to share safe evidence with law enforcement agencies, such as courts, the police, and other forensic groups. Using blockchain to guarantee data immutability, cryptographic security to restrict access, and artificial intelligence (AI) to safeguard data, this approach enhances the privacy, security, and dependability of handling forensic evidence in medical investigations.
Blockchain Technology Applications and Security
Artificial Intelligence in Healthcare and Education
Threshold multi-party fully homomorphic encryption (TMFHE) schemes enable efficient computation to be performed on sensitive data while maintaining privacy. These schemes allow a subset of parties to perform threshold decryption of evaluation results via a distributed protocol without the need for a trusted dealer, and provide a degree of fault tolerance against a set of corrupted parties. However, existing TMFHE schemes can only provide correctness and security against honest-but-curious parties. We construct a compact TMFHE scheme based on the Learning with Errors (LWE) problem. The scheme applies Shamir secret sharing and share resharing to support an arbitrary t-out-of-N threshold access structure, and enables non-interactive reconstruction of secret key shares using additive shares derived from the current set of online participants. Furthermore, the scheme implements commitment and non-interactive zero-knowledge (NIZK) proof techniques to verify the TMFHE operations. Finally, our experiments demonstrate that the proposed scheme achieves active security against malicious adversaries. It overcomes the limitation of existing TMFHE schemes that can only guarantee correct computation under passive semi-honest adversaries.
As a Secure Payment and IoT Cloud Cryptography Architect for Banking Systems, to specialize in designing and implementing cryptographic security solutions for smart cards, contactless payments, and IoT -driven banking infrastructures. Leveraging advanced cryptographic techniques, For data protection and authentication, this paper proposes integrate AES, RSA, ECC, SHA-3, and HMAC while making sure that industry standards like PCI DSS, EMV, ISO 27001, and NIST are followed. Post-quantum cryptography (CRYSTALS-Kyber, Dilithium), zero-knowledge proofs (ZKPs), It should be fluent in cutting-edge technologies such as blockchain-based security for decentralized identification and IoT payments, secure multi-party computing (MPC), and fully homomorphic encryption (FHE). Additionally, this study focus on AI-driven fraud detection, confidential computing, and hardware security modules (HSM, TPMs) to enhance banking cybersecurity resilience. With a commitment to innovation, this research work develop quantum-resistant, privacy-preserving cryptographic frameworks to safeguard financial ecosystems against evolving cyber threats.
The Border Gateway Protocol (BGP) experiences multiple security threats during inter-domain routing such as prefix hijacking and route leaks and man-in-the-middle attacks. Resource Public Key Infrastructure (RPKI) and BGPsec along with other security solutions authenticate networks better but lack protection of network privacy and exhibit weaknesses due to concentration of authority. This paper introduces an integrated ZKP-based Route Verification Framework which uses blockchain technology to establish tamper-resistant privacy-preserving route validation. The framework includes five fundamental elements that provide ZKP proof generation for route credentials and blockchain-based proof storage and automated proof verification with BGP extension and off-chain IPFS-based proof management systems. The system architecture uses zk-SNARKs for cryptographic verifications while it relies on Hyperledger Fabric for decentralized proof validation. The proposed solution achieved superior routing security because it maintains both efficient storage scalability and minimal computational overhead according to performance testing results. The system results show that this framework provides adequate capabilities for actual Internet Service Provider deployments which support decentralized routing across domains while maintaining privacy protection.