Social media platforms rely heavily on user interaction data to personalize content and advertisements, raising concerns regarding user privacy and data misuse. Although regulations such as the General Data Protection Regulation (GDPR) aim to address these concerns, enforcement remains under the control of the platforms themselves. To address these issues, this paper proposes a privacy-preserving recommender system that minimizes personal preference data exposure while maintaining the potential for equivalent personalization accuracy (including for advertisements) through local data processing, which could access the same amount or more of pure user data than server-side models. This system integrates a lightweight client-based machine learning model to infer user preferences locally, combined with Merkle tree-based Zero-Knowledge Proof (ZKP) scheme to anonymously authenticate user requests. The authors develop a working web app prototype and evaluate performance across a range of user devices. Results show minimal latency for the client-based model (under 5 milliseconds on most devices) and diverse proof generation times, ranging from 2.6 seconds to over 18 seconds, depending on hardware capability. Server-side verification remains consistent and fast under 250 milliseconds. Although proof generation latency remains a bottleneck for real-time applications, optimization strategies such as proof-caching, cross-application preferences synchronization, and native implementation provides a promising path toward privacy-preserving personalization in social media systems.
The article presents a comprehensive overview of zero-knowledge proof (ZKP) protocols as a fundamental concept of modern cryptography. The historical background of their emergence and the main properties ensuring reliability and confidentiality, i.e., completeness, soundness, and zero-knowledge — are considered. A classification of protocols into interactive and non-interactive ones is provided, with a special focus on modern solutions such as the zk-SNARK and the zk-STARK. The mathematical foundations of ZKPs are described in detail, including discrete logarithm proofs, the use of homomorphic encryption, polynomial commitments, hashing, and elliptic curves. Practical application areas are analyzed, including cryptocurrencies (Zcash, Ethereum), authentication systems, digital identity, and electronic voting. The advantages of using ZKPs are shown, such as enhanced privacy, reduced need for trusted intermediaries, and strengthened security. At the same time, key challenges are outlined, including scalability, implementation complexity, the problem of trusted setup, and potential vulnerability to quantum computing. It is concluded that zero-knowledge proof protocols are a powerful tool for ensuring confidentiality and reliability of digital systems, while further research is aimed at creating more efficient and quantum-resistant solutions.
This research presents a unified and intelligent security framework for cloud computing environments by integrating Quantum Neural Networks (QNNs), Zero-Knowledge Proof Engines (ZKEs), and Blockchain technology. As the scale and complexity of cloud infrastructures grow, traditional security mechanisms are proving insufficient against advanced cyber threats such as Distributed Denial of Service (DDoS), malware attacks, and Man-in-the-Middle (MITM) exploits. The proposed framework leverages the computational power of quantum systems to enhance the performance of Deep Learning models, enabling faster and more accurate threat detection. ZKEs provide privacy-preserving authentication by verifying user identities without revealing sensitive information, while Block chain ensures data integrity, decentralization, and tamper-proof transaction records. Experimental evaluationconducted using IBM Qiskit and a honey net-integrated Deep Neural Network (DNN) demonstrates a significant improvement in detection accuracy, reduced response time, and enhanced system resilience compared to conventional approaches. The results validate the effectiveness of the multi-layered model in addressing real-world cloud security challenges. This research contributes a scalable, privacy-centric, and quantum-secure architectural foundation for the next generation of cloud-based systems
ChipmunkRing, a practical post-quantum ring signature construction tailored for blockchain environments. Building on our Chipmunk lattice-based cryptographic framework, this implementation delivers compact digital signatures ranging from 20.5 to 279.7KB, with rapid signing operations completing in 1.1-15.1ms and efficient validation processes requiring only 0.4-4.5ms for participant groups of 2-64 members. The cornerstone of our approach is Acorn Verification-a streamlined zero-knowledge protocol that supersedes the classical Fiat-Shamir methodology. This innovation enables linear O(n) authentication complexity using concise 96-byte cryptographic proofs per participant, yielding a remarkable 17.7x performance enhancement for 32-member rings when compared to conventional techniques. Our work includes rigorous mathematical security demonstrations confirming 112-bit post-quantum protection (NIST Level 1), extensive computational benchmarking, and comprehensive support for both standard anonymity sets and collaborative threshold constructions with flexible participation requirements.
N.Shanmuga Priya, M. A. Gopinath, V Gunaseelan., K Guruganesh
This paper presents a new decentralized e-voting architecture designed to create tamper-proof, transparent, and secure election systems using the blockchain and smart contracts. The solution uses a permissioned blockchain structure that is private and Hyperledger Fabric-based in an effort to improve scalability and privacy while retaining the inherent features of decentralization. A zero-knowledge proof (ZKPs) based novel authentication method is implemented to provide eligibility checking and safeguard the privacy of the voter. The smart contracts are aimed at automating ballot tallying, publishing results, and checking their validity. The system is also resistant to traditional channels of attack such as denial-of-service, vote tampering, and voting redundancies based on its distributed ledger and consensus algorithms. A light-weight online interface has been implemented in an effort to promote usability and accessibility, thereby showing an unproblematic voter experience. Experimental results have now made it feasible to deploy the system in organizational and government voting applications, thereby testing its effectiveness within these settings. This method represents a tangible step towards an entirely reliable digital democracy.
Supply chain finance, a critical tool for industrial chain coordination in the digital economy, faces challenges such as centralized identity authentication, data silos, and privacy risks, with traditional models constrained by single-point failures and inefficient data sharing.While blockchain's decentralized and tamper-proof features offer a solution, existing approaches often lack comprehensiveness.To address this, this study proposes two integrated solutions: first, the ZK-SCFI scheme, which leverages Merkle trees for identity storage, Paillier homomorphic encryption for pseudo-identity generation, and zero-knowledge proofs for privacy-preserving verification, effectively avoiding centralized risks and ensuring transaction non-associability; second, the TRU-SABE framework, combining blockchain and IPFS to enhance ciphertext-policy attribute-based encryption (CP-ABE) with keyword search, user revocation, outsourced decryption, and malicious user tracking, addressing traditional limitations like high computational costs and scalability issues.Experiments demonstrate TRU-SABE's superior efficiency, with user-side decryption overhead reduced to 3TE and storage advantages from factor group structures for attribute keys and ciphertexts, while the hybrid architecture alleviates data silos and storage pressure.A prototype system built on Fisco Bcos consortium blockchain, Spring Boot backend, and Vue.js frontend validates core functionalities, including encrypted data sharing and smart contract-based traceability.This work provides a holistic privacy-preserving solution for supply chain finance, with future directions focusing on balancing privacy with regulatory compliance, optimizing multi-authority key management, and expanding system capabilities to enhance security and efficiency.
In the context of a growing shift towards DeFi, the challenge of user identity verification while maintaining privacy remains a daunting task. This paper proposes a model for identity verification based on Zero-Knowledge Proofs (ZKPs) tailored for distributed financial contexts. The model implemented uses cryptographic methods to verify identity claims while maintaining the confidentiality of the personal information, achieving a delicate equilibrium between privacy and financial compliance. The privacy-compliant framework increases security while satisfying legal compliance by removing the need to trust a single party and decreasing exposure of personal information and data. The proposed model improves privacy, verification speed, and fraud resistance compared to conventional and baseline systems.
Md Bokhtiar Al Zami, Md Raihan Uddin, Dinh C. Nguyen
Federated learning (FL) has gained popularity as a privacy-preserving method of training machine learning models on decentralized networks. However to ensure reliable operation of UAV-assisted FL systems, issues like as excessive energy consumption, communication inefficiencies, and security vulnerabilities must be solved. This paper proposes an innovative framework that integrates Digital Twin (DT) technology and Zero-Knowledge Federated Learning (zkFed) to tackle these challenges. UAVs act as mobile base stations, allowing scattered devices to train FL models locally and upload model updates for aggregation. By incorporating DT technology, our approach enables real-time system monitoring and predictive maintenance, improving UAV network efficiency. Additionally, Zero-Knowledge Proofs (ZKPs) strengthen security by allowing model verification without exposing sensitive data. To optimize energy efficiency and resource management, we introduce a dynamic allocation strategy that adjusts UAV flight paths, transmission power, and processing rates based on network conditions. Using block coordinate descent and convex optimization techniques, our method significantly reduces system energy consumption by up to 29.6% compared to conventional FL approaches. Simulation results demonstrate improved learning performance, security, and scalability, positioning this framework as a promising solution for next-generation UAV-based intelligent networks.
Zero-Knowledge Proofs (ZKP) are protocols which construct cryptographic proofs to demonstrate knowledge of a secret input in a computation without revealing any information about the secret. ZKPs enable novel applications in private and verifiable computing such as anonymized cryptocurrencies and blockchain scaling and have seen adoption in several real-world systems. Prior work has accelerated ZKPs on GPUs by leveraging the inherent parallelism in core computation kernels like Multi-Scalar Multiplication (MSM). However, we find that a systematic characterization of execution bottlenecks in ZKPs, as well as their scalability on modern GPU architectures, is missing in the literature. This paper presents ZKProphet, a comprehensive performance study of Zero-Knowledge Proofs on GPUs. Following massive speedups of MSM, we find that ZKPs are bottlenecked by kernels like Number-Theoretic Transform (NTT), as they account for up to 90% of the proof generation latency on GPUs when paired with optimized MSM implementations. Available NTT implementations under-utilize GPU compute resources and often do not employ architectural features like asynchronous compute and memory operations. We observe that the arithmetic operations underlying ZKPs execute exclusively on the GPU's 32-bit integer pipeline and exhibit limited instruction-level parallelism due to data dependencies. Their performance is thus limited by the available integer compute units. While one way to scale the performance of ZKPs is adding more compute units, we discuss how runtime parameter tuning for optimizations like precomputed inputs and alternative data representations can extract additional speedup. With this work, we provide the ZKP community a roadmap to scale performance on GPUs and construct definitive GPU-accelerated ZKPs for their application requirements and available hardware resources.
The emergence of 6G networks enhances the speed and compatibility of Internet-of-Things (IoT) devices in vehicular ad hoc networks (VANETs), leveraging underutilized bands to improve wireless communication and security, though its adaptability may introduce cyber vulnerabilities; to address this, we propose an energy-efficient consortium-based blockchain-enabled heterogeneous (EBH) 6G network for IoT devices, offering secure VANET control through a lattice-based ring signcryption scheme that ensures timely message relaying while preserving vehicle anonymity and cloud data confidentiality, with blockchain blocks formed via secure peer nodes and service provider data; our protocol’s security was rigorously validated through analysis and Python-based implementation, achieving 42.1 ms computational cost and 1026-bit communication overhead, and proving effectiveness across varying block and transaction loads, while guaranteeing key security properties-anonymity, linkable privacy, unforgeability, and confidentiality-even under quantum threats, using lattice-based cryptography, Zero-Knowledge Proofs (ZKP), and blockchain immutability.
Web3 has attracted considerable attention in fields including DeFi, DApps, and NFTs due to its decentralization, enhanced privacy, and user-centricity. However, interoperability and scalability challenges hinder its widespread adoption. While deploying anonymous credentials across Web3 networks to enable cross-network service access is a potential solution to these challenges, existing credential systems remain limited by centralized management, high energy consumption, and credential abuse, making them unsuitable for Web3 environments. To overcome these limitations, we propose a decentralized anonymous functional credential (DAFC) scheme that is efficient, privacy-preserving, and linkable. Unlike existing schemes, DAFC enables users to generate a single proof embedding attributes$x$for requesting services under different access policies. Each provider can use the functional key$sk_{F}$associated with their respective access policy$F$to extract$F(x)$for attribute verification. This significantly reduces authentication computational overhead. Furthermore, DAFC's linkability effectively mitigates credential abuse risks. As an additional contribution, we propose a novel construction of non-interactive zero-knowledge functional proof (fNIZK) based on one-out-of-many proofs and functional encryption for inner products, which is the building block of DAFC. Security analysis demonstrates that DAFC achieves anonymity, unforgeability, and linkability. Performance evaluation shows that DAFC outperforms prior schemes in both computational and communication overhead when requesting at least 6 services with distinct access policies.
Journal of Theoretical and Applied Information Technology
The insurance sector is being transformed through the combination of artificial intelligence (AI) and blockchain technologies. This study proposes the AI-Blockchain Hybrid Smart Contract Model (AIBSCM), which combines AI-based fraud detection with blockchain-based smart contracts to allow for automated insurance claim processing. A synthetic dataset of 1,000 insurance claims was used to train a random forest model, which achieved 92% accuracy on training data; however, real-world testing revealed difficulty in detecting fraudulent claims from under-represented categories. A blockchain simulation was conducted to demonstrate the secure storage and automated execution of claims, with smart contracts giving transparency and immutability. The architecture integrates decentralised oracles, zero-knowledge proofs (ZKPs), federated learning, and a DAO governance mechanism to provide a privacy-conscious, decentralised, and robust solution for the insurance business. Subsequent study will look at real-world deployment and integration with regulations. The integration of these technologies seeks to address traditional insurance systems' issues, such as data privacy concerns and a lack of transparency. By investigating real-world deployment and regulatory compliance, this model has the potential to transform the insurance business by delivering a safe and efficient method for dealing with false claims. This innovative method has the potential to boost client trust while also streamlining insurance company operations. Overall, the combination of blockchain and privacy-conscious technology might result in increased reliability and a transparent insurance sector.
Transport Infrastructure Ireland (TII) commissioned a cooperative intelligent transport system (C-ITS) pilot. Although the European C-ITS Security Credential Management System (EU CCMS) and European Telecommunications Standards Institute Public Key Infrastructure (ETSI PKI) standards offer a foundation for secure Vehicle-to-everything (V2X) communication, challenges persist in scalability, latency, revocation, and misbehavior detection. This work proposes a hybrid framework, with improvements including blockchain-based revocation, decentralized trust models, and privacy preservation using pseudonym rotation and zero-knowledge proofs, thus extending existing standards.
Zero-knowledge proofs (ZKPs) are increasingly deployed in domains such as privacy-preserving authentication, verifiable computation, and secure finance. However, authoring ZK programs remains challenging: unlike conventional software development, ZK programming manifests a fundamental paradigm shift from \textit{imperative computation} to \textit{declarative verification}. This process requires rigorous reasoning about finite field arithmetic and complex constraint systems (which is rare in common imperative languages), making it knowledge-intensive and error-prone. While large language models (LLMs) have demonstrated strong code generation capabilities in general-purpose languages, their effectiveness for ZK programming, where correctness hinges on both language mastery and constraint-level reasoning, remains unexplored. To address this gap, we propose \textsc{ZK-Eval}, a domain-specific evaluation pipeline that probes LLM capabilities on ZK programming at three levels: language knowledge, algebraic primitive competence, and end-to-end program generation. Our evaluation of four state-of-the-art LLMs reveals that while models demonstrate strong proficiency in language syntax, they struggle when implementing and composing algebraic primitives to specify correct constraint systems, frequently producing incorrect programs. Based on these insights, we introduce \textsc{ZK-Coder}, an agentic framework that augments LLMs with constraint sketching, guided retrieval, and interactive repair. Experiments with GPT-o3 on Circom and Noir show substantial gains, with success rates improving from 20.29\% to 87.85\% and from 28.38\% to 97.79\%, respectively. With \textsc{ZK-Eval} and \textsc{ZK-Coder}, we establish a new basis for systematically measuring and augmenting LLMs in ZK code generation to lower barriers for practitioners and advance privacy computing.
Open access
2 source records
Mathematics, Computing, and Information Processing
Internet of Underwater Things (IoUT) introduces critical security challenges, especially for protecting distributed infrastructures in resource-constrained environments. Conventional asymmetric and centralized authentication models are unsuitable due to computational and communication overhead, while symmetric approaches lack robustness without trusted storage or hardware. We propose a non-interactive, asynchronous authentication protocol based on NIZKP, combining PUFs-derived secrets with decentralized identifiers on a distributed ledger. This approach enables direct node authentication with cryptographically verifiable identity binding, minimal resource usage, offline verification, and full support for asynchronous operation in constrained environments. The protocol is formally analysed and implemented on COTS hardware without additional secure components. Evaluation shows low energy consumption (827.2 mJ), minimal communication overhead (113 B, 1.513s, 817.9 mJ), and reasonable execution times (worst case ≈ 5.310s), outperforming state-of-the-art solutions in the first four metrics.
Globalized supply chains are strained by fragmented data, multi-tier opacity, counterfeit risks, and costly disputes. Blockchain—a shared, append-only ledger—has been proposed to enhance transparency, traceability, and operational efficiency, yet real-world adoption reveals both breakthroughs and bottlenecks. This paper develops a deploymentminded view that integrates GS1 EPCIS/CBV standards for interoperable event data, permissioned ledgers for governance, and privacy-preserving proofs (zero-knowledge) to reconcile transparency with business confidentiality. We synthesize evidence from systematic reviews and flagship pilots (e.g., Walmart–IBM Food Trust) and contrast them with lessons from initiatives that wound down (e.g., TradeLens), extracting adoption patterns, KPI impacts, and failure modes. We then describe a reference methodology—data acquisition via EPCIS events, Fabric-based channels, and role-based access—plus an evaluation rubric for trace time, recall precision, dispute cycle time, and data-reconciliation costs. Results from literature-anchored benchmarks indicate orders-of-magnitude traceability lead-time (TLT) reductions (days → seconds) and measurable reductions in manual reconciliation, with gains contingent on standards compliance and high-quality “oracle” data. Finally, we map future directions—zk-proof rollups, interoperable digital product passports, and policy-aligned sustainability metrics—alongside candid limitations around ecosystem incentives, privacy, scalability, and data veracity. We conclude that blockchain can shift chains from reactive to verifiable and auditable networks when combined with data standards, sound governance, and selective privacy technologies rather than “full transparency” alone.
The rapid advancement of artificial intelligence (AI) and large language models (LLMs) is profoundly reshaping higher education, shifting from institution-centered paradigms to learner-centric personalized learning environments (PLEs). However, PLEs face critical challenges in identity management, including data breaches, unauthorized access, and interoperability barriers, which undermine security and trust. This study proposes the Blockchain-based Student Identity Management System (BSIMS), a conceptual model integrating blockchain technology, xAPI standards, and OAuth2 protocols to uphold confidentiality, integrity, availability, authenticity, and non-repudiation (CIAAN) principles. Grounded in the Technology Acceptance Model (TAM) and Information Systems Success Model (ISSM), BSIMS was validated through mixed-methods research involving 90 students and 16 experts from five Yunnan Province universities. Results demonstrate BSIMS' superiority in user satisfaction (explaining 81.7% variance), CIAAN performance (M=4.58 vs. 3.18 for traditional systems, p<0.001), and reliability (zero downtime, 0.3 ms response time). Ethical and legal implications, such as immutability conflicts with privacy rights, are addressed via zero-knowledge proofs and off-chain storage. BSIMS offers a scalable framework for secure PLEs, advancing educational informatization in Yunnan and beyond.
Christoph Hochrainer, Valentin Wüstholz, Maria Christakis
Zero-knowledge virtual machines (zkVMs) are increasingly deployed in decentralized applications and blockchain rollups since they enable verifiable off-chain computation. These VMs execute general-purpose programs, frequently written in Rust, and produce succinct cryptographic proofs. However, zkVMs are complex, and bugs in their constraint systems or execution logic can cause critical soundness (accepting invalid executions) or completeness (rejecting valid ones) issues. We present Arguzz, the first automated tool for testing zkVMs for soundness and completeness bugs. To detect such bugs, Arguzz combines a novel variant of metamorphic testing with fault injection. In particular, it generates semantically equivalent program pairs, merges them into a single Rust program with a known output, and runs it inside a zkVM. By injecting faults into the VM, Arguzz mimics malicious or buggy provers to uncover overly weak constraints. We used Arguzz to test six real-world zkVMs (RISC Zero, Nexus, Jolt, SP1, OpenVM, and Pico) and found eleven bugs in three of them. One RISC Zero bug resulted in a $50,000 bounty, despite prior audits, demonstrating the critical need for systematic testing of zkVMs.
Mohammed Ziaul Haider, Tayyaba Noreen, Mishah Uzziél Salman, Marcos Dias de Assunção · 5 authors
Cross-chain bridges and oracle DAOs represent some of the most vulnerable components of decentralized systems, with more than 2.8 billion lost due to trust failures, opaque validation behavior, and weak incentives. Current oracle designs are based on multisigs, optimistic assumptions, or centralized aggregation, exposing them to attacks and delays. Moreover, predictable committee selection enables manipulation, which threatens data integrity across chains. We propose V-ZOR, a verifiable oracle relay that integrates zero-knowledge proofs, quantum-grade randomness, and cross-chain restaking to mitigate these risks. Each oracle packet includes a Halo 2 proof verifying that the reported data was correctly aggregated using a deterministic median. To prevent committee manipulation, VZOR reseeds its VRF using auditable quantum entropy, ensuring unpredictable and secure selection of reporters. Reporters stake once on a shared restaking hub; any connected chain can submit a fraud proof to trigger slashing, removing the need for multisigs or optimistic assumptions. A prototype in Sepolia and Scroll achieves sub-300k gas verification, one-block latency, and a $\mathbf{1 0} \times$ increase in collusion cost. V-ZOR demonstrates that combining ZK attestation with quantum-randomized restaking enables a trust-minimized, high-performance oracle layer for cross-chain DeFi.
Abstract Background Children with subtotally resected pediatric low-grade glioma (pLGG) often face multiple lines of treatment, which are seldom capable of eliminating the entire tumor. Genomics-based biomarkers are often used to select targeted therapies, but this paradigm only yields overall response rates of ∼50% optimally. Functional precision medicine (FPM), where patient-specific therapeutic efficacy is evaluated by directly treating individuals’ tumor outside their body, can predict individualized drug responses for some cancers, but pLGG is notoriously difficult to maintain outside the body, limiting development of FPM for pLGG. Methods We describe what is, to our knowledge, the first platform that can maintain, treat, and analyze zero-passage pLGG tumor tissue ex vivo , facilitating FPM testing. We engraft pLGG tumors onto a previously validated organotypic brain slice culture (OBSC) platform. After ensuring reproducible engraftment and maintenance of living pLGG tumor tissue on OBSCs, we measured MAPK pathway response to targeted therapies via immunoblotting. We then measured tumor ex vivo response to targeted therapies. Results Each zero-passage pLGG tumor tissue specimen exhibited reproducible growth on the OBSC platform. Western blot demonstrated each BRAF KIAA1549 fusion+ tumor exhibited expected paradoxical MAPK upregulation to dabrafenib treatment. Two of three tumors demonstrated cytotoxicity from trametinib as predicted, whereas one tumor did not. No clinical correlates were measured in this proof-of-concept study, though this mixed response to MEK inhibition may be in line with real-world clinical responses. Conclusion The OBSC platform supports ex vivo maintenance of passage-zero pLGG tumor tissue and enables personalized drug screening to yield a new functional biomarker of pLGG drug response.
With the rapid development of the Internet of Things (IoT), the security and privacy of personal data has received widespread attention. Federated learning models protect personal privacy data through distributed collaborative training models, but it has been shown that personal privacy data can be inferred from uploaded parameters. Federated learning models also face the challenges of privacy leakage risk, computational inefficiency and lack of verifiability. Existing differential privacybased federated learning models and homomorphic encryptionbased federated learning models are unable to balance model accuracy and security. They also face the problem of inefficient computation of client-side local data and high communication overhead. Therefore, in this paper, we propose a federated learning framework (RGC-FL) based on Re-randomizable Garbled Circuits (RGC), which achieves a balance between privacy protection and computational efficiency through dynamic encryption and re-randomization techniques. The model updates are first encrypted at the client using the obfuscated circuits and then uploaded to the server, and then the ciphertext updates are aggregated by the re-randomization technique to avoid the leakage of the original data. Secondly, the client verifies the correctness of the server’s aggregation results by zero-knowledge proof. Finally based on DDH assumption and Kilian randomization technique to defend against hybrid attacks in dynamic input scenarios. We experimentally show that the model accuracy of RGC-FL on MNIST and CIFAR-10 datasets is 97.3% and 83.9%, respectively, which is close to plaintext federated learning and significantly outperforms the Differential Privacy (DP-FL) and Fully Homomorphic Encryption scheme (FHE-FL). In terms of efficiency, the training time for a single round is only 32% of that of FHE-FL (12.4 sec vs. 38.7 sec), and the communication overhead is reduced by $80 \%(5.2 \mathrm{MB}$ vs. 25.6 MB). This paper provides an efficient and secure solution for federated learning in highly privacy-sensitive domains and promotes the wide application of AI under compliance requirements.
This paper presents an enhanced blockchain-based salary certification system that leverages FISCO BCOS, Node.js, and MySQL to address critical challenges in traditional salary management systems. The system introduces more substantial advancements in blockchain topology design, performance optimization, and security mechanisms. It features a hierarchical consortium blockchain architecture that balances decentralization with operational efficiency, ensuring robust security and transparency. The system employs advanced techniques such as dynamic validator set management, batch transaction processing, and parallel validation pipelines to achieve higher transaction throughput and reduced latency. Additionally, it integrates zero-knowledge proofs and a multi-active data center architecture for enhanced data protection and disaster recovery. Performance evaluation demonstrates significant improvements in transaction throughput and latency, with the system achieving high success rates in both open and query type tests. The modular design allows for flexible deployment across various organizational structures, providing a comprehensive solution for secure, transparent, and efficient salary data management.
Traceable ring signatures (TRSs) allow a signer to create a signature that maintains anonymity while enabling traceability if needed. It merges the characteristics of traditional ring signatures with the ability to trace signers, making it ideal for applications that demand both confidentiality and accountability. In a TRS scheme, a ring of potential signers generates a signature on a message without disclosing the actual signer’s identity. However, the identity can be traced if the signer uses the same tag for multiple signatures. This paper introduces a novel formal construction of TRS under universally composable (UC) security. We integrate verifiable random functions (VRFs) and zero-knowledge proofs for membership, employing Pedersen commitments. Our signature schemes maintain a logarithmic size while preserving the UC security guarantees. Additionally, we explore the potential to extend the property of one-time anonymity in TRS to K-time anonymity.
Mahendran Chinnaiah, A. Kumar Chandra Gupta, Saurabh Srivastava, Ashok Ghimire
At a time when data privacy laws and cyber-attacks are on the rise, Zero-Knowledge Proofs (ZKPs) and Artificial Intelligence (AI) hold the potential of a transformational paradigm of safe (privacy-preserving) machine learning (ML) inferences. In this paper, we present a new architecture that facilitates Zero-Knowledge AI, in which sensitive data inputs and internal model parameters remain unknown during the model inference procedure across distributed ecosystems. The proposed framework can help preserve privacy standards like GDPR and HIPAA, inference accuracies, and scalability of these inferences by utilising mechanisms to observe cryptographic zero-knowledge protocols, as well as federated learning protocols. We describe the construction of ZK-friendly models to apply to neural inference pipelines, efficient zk-SNARK-based model validation, decentralized trusting schemes, and privacy-respecting model auditing. Testing over a variety of healthcare and financial datasets indicates that our Zero-Knowledge AI solution results in high privacy guarantees with limited throughput losses. The work provides a strong basis on how to implement trusted and privacy-first AI systems in the real life and distributed operating environment.