Mingwang Zhang, Liming Zhang, Tao Tan, Yang Zhao-jun · 5 authors
ABSTRACT With the rapid advancement of autonomous driving, the privacy and credibility of high‐definition (HD) maps, which serve as an essential foundation for driving safety, are receiving increasing attention. Traditional ciphertext‐domain digital watermarking technology encounters high computational overhead and risks of privacy leakage, making it challenging to balance data security, privacy protection, and trustworthiness verification. Against this background, a zero‐knowledge watermark (ZKW) algorithm based on compressed sensing is proposed. First, the high‐precision map data in OpenDrive format is dynamically encrypted using DNA‐based techniques to enhance data security and privacy. Secondly, to ensure the credibility of data verification, a zero‐knowledge watermark is generated using compressed sensing and embedded into the attribute values of ciphertext‐domain data as invisible characters. Experimental results demonstrate that the proposed ZKW scheme is commutative with the encryption scheme and can achieve zero‐knowledge proof (ZKP) in both ciphertext and plaintext domains. Furthermore, the scheme exhibits excellent robustness against various security threats, including geometric attacks, cropping attacks, and combined attacks.
Advanced Steganography and Watermarking Techniques
Damla Sarıçelik, Mohaned Chraiti, Albert Lévi, Özgür Erçetin
The Open Radio Access Network (O-RAN) paradigm fosters multi-vendor interoperability, allowing modules from different vendors to cooperatively handle network functions, such as temporary data processing or sensor data collection for network operations optimization. However, this integration agility introduces the risk of selecting suboptimal or adversarial modules, leading to moral hazard. Traditional Moral Hazard testing approaches typically rely on a benchmarking data set in addition to historical performance score. However, they deemed impractical, as vendor-supplied modules may not reveal their outputs before deployment, and the network may lack direct access to reference results for validation. This challenge is further compounded by the dynamic nature of network elements and AI-driven models, whose performance can degrade over time due to malicious tampering, obsolescence, or device deterioration, making historical quality assessments ineffective. In this paper, we address the challenge of identifying legitimate vendor-supplied modules among adversarial ones, with respect to a given network functionality/operation, in the absence of benchmarks. We propose a benchmark-free test framework that detects and eliminates adversarial modules using a methodology inspired by the WereWolf game, combined with zero-knowledge proof techniques. Monte Carlo simulations demonstrate that our approach effectively removes adversarial entities while preserving the privacy of legitimate modules.
Purpose: The framework of the Autonomous Vehicles (AVs) is facilitated by modern communication systems. In Real-Time (RT), the data is communicated to one another, and it is encouraged by AV. This AV also communicates with organizations stationed along the roadway, and navigates without human intervention. The dynamic and decentralized communication between vehicles and Roadside Units (RSUs) is integrated in Vehicular Ad Hoc Networks (VANETs). Then, there is no centralized structure for utilization in VANET. This distributed system faces difficulties in 2 areas: Authentication and security. The susceptibility to the network is increased by the unpredictable and risky features of AV, because there is currently no robust authentication system in place for multi-broadcast situations. Hence, the network is susceptible to security breaches, illegal access, data tampering, and service interruptions. During Handover Authentication (HA) between RSUs, critical security vulnerabilities are introduced by AV communication in ad-hoc networks, because of their dynamic topology and mobility patterns.Methodology: To address these issues, this study proposes a HA system for ad hoc AVs that uses blockchain technology. Using distributed controllers and Zero Knowledge Proofs (ZKPs), the proposed methodology enables rapid and safe authentication of AVs during handover between RSUs. To optimize authentication, decentralized Smart Network Controllers (SNCs) were used by the suggested method. This suggested method also eliminates the dependency on centralized entities and mitigates Single Point of Failure (SPoF) vulnerabilities.Findings: A private Blockchain network implementation makes the system transparent and immutable while providing tamper-proof storage for vehicle data. Simulation results demonstrate that the protocol achieves a 30.4% reduction in authentication latency, 27.8% lower packet loss rate, and a 23.5% improvement in throughput compared to EMT and GMT baseline protocols. Additionally, the system sustains a 95.2% success rate in mutual authentication under high vehicle density and maintains security integrity against impersonation, Sybil, and replay attacks.Originality/Value: The suggested protocol also mitigates SPoF risk of centralized systems and offers smooth Vehicle-to-Everything (V2X) services without charging any transaction fee. This method provides strong and scalable security for the communication of AVs in smart city systems.
The Lattice Isomorphism Problem (LIP) is an emerging foundation for post-quantum cryptography thanks to the pioneering work of Ducas and van Woerden (EUROCRYPT '22). That work lays the foundation for LIP in cryptography with a zero-knowledge proof of knowledge (ZKPoK), a key-encapsulation mechanism, and a digital signature scheme which is further developed into the efficient signature Hawk (ASIACRYPT '22) using structured lattices. However, as compared to the development of lattice-based cryptography from the Short Integer Solution (SIS) and Learning with Errors (LWE) problems, LIP-based cryptography is yet to cover a rich variety of functionalities beyond the two basic ones: encrypting and digitally signing messages. This work is an effort to extend the landscape of LIP-based cryptography from the above basic primitives to more advanced ones by adapting techniques used in SIS-based and LWE-based primitives to LIP. We provide a public-key encryption (PKE) which encrypts plaintexts of integral vectors, and it comes with a zero-knowledge proof of plaintext knowledge. We use this PKE as a commitment scheme in the construction of a ZKPoK for quadratic relations, so this ZKPoK has a straightline extractor naturally. Using the same ZKPoK in non-interactive mode by Fiat-Shamir transformation, we introduce the first LIP-based blind signature scheme which is the blinded version of the digital signatures of Ducas and van Woerden. The security of our scheme stems from a new one-more Close Vector Problem (omCVP) assumption. This assumption is arguably an analogue of the one-more-SIS assumption by Agrawal et al. (CCS '22) and the one-more Short Vector Problem in Hawk signatures. To ensure confidence in omCVP, we provide a cryptanalysis attempt and convince that our parameter choice is in the safe zone.
Abstract: Ensuring the integrity, privacy and accessibility of electoral system remains a critical global challenge. This paper proposes a secure blockchain based e-voting framework enhanced with anti-spoofing facial recognition for voter authentication and zero-knowledge proofs to preserve voter anonymity while enabling verifiable results. The proposed system integrates seamlessly with existing election infrastructure, allowing transparent vote recording on a tamper-resistant distributed ledger while preventing identity fraud through advanced biometric anti-spoofing techniques. Zero Knowledge Proofs enable vote verification without revealing individual choices, ensuring both privacy and trust. By combining blockchain’s immutability, biometric security and cryptographic privacy guarantees, this approach addresses vote tampering, impersonation, and transparency concerns, offering a scalable , auditable, and privacy-preserving solution for modern elections. Keywords: Blockchain, E-Voting, Anti-Spoofing, Facial Recognition, Zero Knowledge Proofs, Election Security, Privacy preserving systems.
Open access
Internet Traffic Analysis and Secure E-voting
Blockchain Technology Applications and Security
Advanced Steganography and Watermarking Techniques
In the rapidly evolving landscape of blockchain technology, the twin challenges of scalability and security remain significant obstacles to widespread adoption. Traditional blockchain architectures struggle to balance the increasing demands for transaction throughput and the imperative of maintaining robust security measures. This work addresses these limitations by proposing an innovative model that integrates advanced privacy mechanisms, rigorous security analysis, and scalability enhancements to forge a more resilient and efficient blockchain framework. The cornerstone of our model is the introduction of ZeroKnowledge Proofs (ZKPs) to enhance user privacy significantly. By enabling transaction verification without revealing sensitive information, ZKPs mitigate information leakage and boost transaction confidentiality. Our findings suggest an estimated 15% improvement in privacy levels, marking a substantial advancement over existing methods that often compromise user privacy for transparency. Addressing the security aspect, we employ Temporal Logic of Actions Plus (TLA+) for formal verification of the blockchain protocol. This method allows us to model the blockchain's behavior systematically, ensuring its correctness, safety, and liveness even under adverse conditions such as Byzantine faults. Our analysis reveals a 98% success rate in detecting and thwarting Byzantine behaviors, thereby substantiating the robustness of our proposed model against a range of security threats. To tackle the issue of scalability, we introduce adaptive sharding with dynamic load balancing. This approach not only partitions the network into manageable shards but also optimizes transaction processing by adapting to changes in transaction volume and network congestion. Our results prove a 20% increase in transaction throughput and a 25% decrease in network latency, showcasing the effectiveness of adaptive sharding in enhancing blockchain scalability and performance.
Aiming at the problems of insufficient integrity assurance, high risk of privacy leakage, and low cross-departmental circulation efficiency in the field of government data security, this study proposes an optimized blockchain solution integrating an improved Practical Byzantine Fault Tolerance (PBFT) consensus algorithm and dynamic Attribute-Based Encryption (ABE). By constructing a government data security storage model, the data layer's hash verification mechanism (SHA-256 + Merkle Patricia Tree) and network layer's P2P protocol (with packet loss retransmission) are optimized based on typical government blockchain platforms (e.g., Nanjing Electronic License Sharing Platform). Experiments with 200 nodes simulated government networks, comparing TPS, data verification delay, and privacy protection before and after optimization. The results show that after optimization: TPS increased by 37.2% (from 89.6 to 123.0), cross-departmental data verification time was shortened to 0.42 seconds, and zero-knowledge proof was used to realize anonymized query of sensitive fields (such as ID card numbers and real estate information). The research indicates that this technical solution can effectively resolve the contradictions among integrity, privacy, and circulation of government data, providing technical references for the engineering implementation of government blockchain systems.
Madhavi Repe, Dr. Nilakshi Rajule, Vandana Katarwar, Ankita Bombatkar
In response to the growing demand for secure and transparent digital elections, this paper presents a blockchain based Smart Election System that leverages advanced cryptographic and artificial intelligence techniques to ensure privacy, scalability, and verifiability. The proposed system integrates multi-factor authentication, Zero-Knowledge Proofs (ZKPs), smart contracts, and a sharded blockchain ledger to enable real-time, tamper-proof voting. Additionally, an Artificial intelligence based anomaly detection module monitors voting behaviour to flag suspicious patterns.Experimental evaluation demonstrates that the system achieves an average vote transaction latency of 1100ms with sharding, compared to 2800ms without it. The throughput increases from 130 to 220 votes/sec when sharding is enabled. ZKP integration ensures privacy at the cost of a moderate increase in validation time from 130ms to 230ms. The anomaly detection model, based on supervised learning, attained 92% precision, 88% recall, and an F1-score of 90%, ensuring proactive fraud detection. These results confirm the system’s effectiveness in delivering a smart scalable, private, and trustworthy e-voting platform suitable for national and institutional elections.
As smart cities continue to evolve through the integration of IoT, AI, and big data analytics, the complexity and scale of cybersecurity challenges have intensified. This chapter examines key vulnerabilities ranging from data privacy breaches to cyber-physical system attacks and highlights the critical role of modern cryptographic technologies in addressing these risks. Techniques such as encryption, blockchain, PKI, homomorphic encryption, zero-knowledge proofs, and quantum cryptography are analyzed for their potential to strengthen smart city infrastructures. Drawing on recent case studies and emerging research, we further explore future directions, including post-quantum security and AI-driven cybersecurity frameworks. The findings of this chapter suggest that strategically embedding cryptographic solutions is essential for building secure, resilient, and citizen-centered smart cities capable of withstanding both current and emerging cyber threats.
This paper studies the practical aspects of adding zero-knowledge proofs of vote correctness to Internet voting, specifically to the IVXV system used in Estonia. We discuss various available alternatives and present a concrete instantiation based on Bulletproofs together with implementation details and benchmarking results. As IVXV currently uses the ElGamal cryptosystem with a 3072-bit prime modulus for vote encryption, but Bulletproofs work most efficiently on elliptic curves, a group switching solution is also implemented and benchmarked. Despite all the extra work required, our solution is very performant and well capable of sustaining the load of votes, even during peak vote submission periods.
Md Mehedi Hasan Emon, Md. Mazid-Ul-Haque, Most. Sharmin Ara Chowdhury, Kh. Mustafizur Rahman
This chapter explores the critical role of advanced cryptographic technologies in securing smart city applications across domains such as e-health, e-commerce, and e-banking. As urban environments become increasingly digitized, safeguarding sensitive data, ensuring transaction integrity, and protecting user privacy emerge as paramount challenges. The chapter explores a spectrum of cryptographic solutions, including blockchain, homomorphic encryption, zero-knowledge proofs, and quantum cryptography, that address these complexities. Through detailed analysis and real-world case studies, it highlights practical implementations and emerging trends that reinforce trust and resilience in smart city infrastructures. Moreover, the chapter discusses the policy and ethical dimensions necessary to balance security with usability and transparency. Finally, it provides a comprehensive framework for integrating cryptographic safeguards into the evolving digital fabric of smart cities, ensuring secure, scalable, and citizen-centric urban services.
Maria Nuțu, Giorgi Akhalaia, Răzvan Bocu, Maksim Iavich
Commitment schemes represent foundational cryptographic primitives enabling secure verification protocols across diverse applications, from blockchain systems to zero-knowledge proofs. This paper presents a systematic survey of vector, polynomial, and functional commitment schemes, analyzing their evolution from classical constructions to post-quantum secure alternatives. We examine the strengths and limitations of RSA-based, Diffie–Hellman, and lattice-based approaches, highlighting the critical shift toward quantum-resistant designs necessitated by emerging computational threats. The survey reveals that while lattice-based schemes (particularly those using the Short Integer Solution problem) offer promising security guarantees, they face practical challenges in proof size and verification efficiency. Functional commitments emerge as a powerful generalization, though their adoption is constrained by computational overhead and setup requirements. Key findings identify persistent gaps in adaptive security, composability, and real-world deployment, while proposed solutions emphasize optimization techniques and hybrid approaches. By synthesizing over 90 research works, this paper provides both a comprehensive reference for cryptographic researchers and a roadmap for future developments in commitment schemes, particularly in addressing the urgent demands of post-quantum cryptography and decentralized systems.
The ever-growing dependency on blockchain technology to secure e-evidence in a forensic investigation will involve building architecture that is intrinsically tamper-proof and at the same time optimized contextually for investigative purposes. Most of the extant blockchain forensic frameworks incorporate static evaluation models and a monolithic approach to consensus, rendering them ill-suited to the dynamic forensic context of various event sensitivity, legality requirements, and auditability demands. Existing barriers to practical deployment in high-stakes forensic environments have been created. To begin addressing these gaps, this work presents Forensic-Driven Blockchain Evaluation and Simulation Architecture (ForBESA), which provides a complete simulation-based evaluation framework to com- pare Byzantine Fault Tolerant (BFT), Directed Acyclic Graph (DAG), and Proof-of- Stake (PoS) blockchains against forensic key performance indicators (KPIs). This framework consists of five novel modules. First, the Context-Aware KPI Weighting Engine (CAKWE) develops dynamic generation of KPI weight vectors using forensic incident metadata through the use of a decision tree classifier. Second, the Temporal Provenance DAG Tracker (TPDT) constructs enriched DAGs embedding trans- action timelines and investigator metadata to enhance traces’ future availability. Third, evidence routing across Hyperledger, IOTA, and Ethereum 2.0 will be simulated within the Hybrid Ledger Simulation Module under individual KPIs weighted with forensic considerations. Fourth, the Performance-Forensic Tradeoff Analyzer (PFTA) employs a utilitybased optimization and Pareto analysis to identify architecture suitability based on forensic depth versus performance trade-offs. Finally, the Chain-of-Custody Cryptographic Verifier (C3V) ensures evidence integrity and legal admissibility using smart contracts and zero- knowledge proofs. The improved forensic effectiveness and trace reconstruction up to 98.1% accuracy and 100% tamper detection are scantly recorded in the experiments. This study creates the first model of its kind regarding forensic-aware blockchain evaluation. The system provides precise, legally compliant, and context-responsive digital investigations in process.
The rapid advancement of quantum computing poses significant challenges to conventional cryptography, necessitating the adoption of post-quantum cryptography (PQC) solutions. This chapter proposes a Post-Quantum Lattice Security (PQLS) system for protecting power plant data in smart cities. It integrates Kyber for secure key exchange, Falcon for quantum-resistant digital signatures, ZKP for efficient authentication without revealing sensitive data, and JSON-LD for standardizing the format of data received from different smart meters. To evaluate the security of the proposed framework, we analyze its resistance to various threats, such as side-channel and message recovery attacks. We measured key performance indicators. The results showed an average CPU utilization of 2.4592 MS, memory consumption averaging 1843.899 KB, an execution time of 2.45 MS, and a level averaging 66.27677. This demonstrates that our proposed system offers high security and efficiency, making it a practical solution for protecting electrical infrastructure in smart cities in the quantum era.
Fine-tuning large language models (LLMs) is crucial for adapting them to specific tasks, yet it remains computationally demanding and raises concerns about correctness and privacy, particularly in untrusted environments. Although parameter-efficient methods like Low-Rank Adaptation (LoRA) significantly reduce resource requirements, ensuring the security and verifiability of fine-tuning under zero-knowledge constraints remains an unresolved challenge. To address this, we introduce VeriLoRA, the first framework to integrate LoRA fine-tuning with zero-knowledge proofs (ZKPs), achieving provable security and correctness. VeriLoRA employs advanced cryptographic techniques -- such as lookup arguments, sumcheck protocols, and polynomial commitments -- to verify both arithmetic and non-arithmetic operations in Transformer-based architectures. The framework provides end-to-end verifiability for forward propagation, backward propagation, and parameter updates during LoRA fine-tuning, while safeguarding the privacy of model parameters and training data. Leveraging GPU-based implementations, VeriLoRA demonstrates practicality and efficiency through experimental validation on open-source LLMs like LLaMA, scaling up to 13 billion parameters. By combining parameter-efficient fine-tuning with ZKPs, VeriLoRA bridges a critical gap, enabling secure and trustworthy deployment of LLMs in sensitive or untrusted environments.
This paper presents a comprehensive literature review on the application of blockchain technology in ensuring data integrity and security within decentralized applications (dApps). Blockchain, with its inherent features such as immutability, decentralization, and transparency, offers a robust framework for safeguarding data across various sectors, including finance, healthcare, and supply chain management. Through an extensive qualitative analysis of existing studies, this research explores how cryptographic techniques, consensus mechanisms, and blockchain's distributed nature contribute to securing data in decentralized environments. The review examines key findings from the literature, including the integration of advanced cryptographic methods such as zero-knowledge proofs and homomorphic encryption, which enhance data privacy while maintaining integrity. Furthermore, the study discusses the challenges of scalability, energy consumption, and off-chain data security in blockchain-based systems, identifying areas for future research. The review also highlights the importance of hybrid blockchain models and scalable consensus algorithms in addressing the limitations of current blockchain frameworks. Overall, this paper contributes to the growing body of knowledge on blockchain-based data security and integrity in decentralized applications and offers recommendations for further research to enhance the scalability, efficiency, and environmental sustainability of blockchain systems.
Observational zero-inflated count data arise in a wide range of areas such as genomics. One of the common research questions is to identify causal relationships by learning the structure of a sparse directed acyclic graph (DAG). While structure learning of DAGs has been an active research area, existing methods do not adequately account for excessive zeros and therefore are not suitable for modeling zero-inflated count data. Moreover, it is often interesting to study differences in the causal networks for data collected from two experimental groups (control vs treatment). To explicitly account for zero-inflation and identify differential causal networks, we propose a novel Bayesian differential zero-inflated negative binomial DAG (DAG0) model. We prove that the causal relationships under the proposed DAG0 are fully identifiable from purely observational, cross-sectional data, using a general proof technique that is applicable beyond the proposed model. Bayesian inference based on parallel-tempered Markov chain Monte Carlo is developed to efficiently explore the multi-modal posterior landscape. We demonstrate the utility of the proposed DAG0 by comparing it with state-of-the-art alternative methods through extensive simulations. An application in a single-cell RNA-sequencing dataset generated under two experimental groups finds some interesting results that appear to be consistent with existing knowledge. A user-friendly R package that implements DAG0 is available at https://github.com/junsoukchoi/BayesDAG0.git.
Paper define innovative approach to unify authentication across Web2 and Web3 ecosystems by using biometric-driven decentralized identifiers (DIDs). The framework employs zero-knowledge attestations (ZKPs) to ensure privacy during verification processes [7], [11] and utilizes Chainlink's Cross-Chain protocol related toInteroperability(CCIP) for flawless operation across multiple blockchains [17]. To enhance liveness detection, we incorporate federated learning to eliminate centralized storage of sensitive biometric data [19]. A novel contribution is the Biometric Soulbound Token (BST), a non-transferable NFT that securely stores hashed facial data [5]. Also, quantum-resistant ZKPs are used to verify biometric matches without exposing raw inputs [14]. The DIDs function cohesively across Ethereum, Polygon, and Solana. Experimental results demon- strate a 99.2% authentication accuracy, a 1.3 -second latency, and full compliance with GDPR. By empowering users with control over their biometric data, this framework bridges centralized and decentralized platforms, enabling secure and efficient identity management.
This chapter proposes a Trustworthy Federated Identity Management (TFIM) framework developed to overcome user identity management challenges across multiple blockchain networks. The research work focuses on solving key issues, such as the lack of unified trust frameworks, insufficient cross-chain identity verification, and limited privacy-preserving mechanisms for cross-chain data sharing. TFIM allows secure DApp interoperability by assessing participant trustworthiness across blockchain networks with the integration of zero-knowledge proofs and federated learning techniques. Performance evaluation shows that TFIM processes 44,041 transactions/s with 15% degradation under real-world conditions, supports 1000 concurrent users, and handles 100 cross-chain authentications/s across up to 15 interconnected networks. Although TFIM provides better privacy protection and more advanced cross-chain capabilities than the Blockchain-Based Federated Identity Framework (BFIF), it comes at the cost of computational overhead and registration/authorization speed. The results suggest potential directions for future optimization while maintaining TFIM’s robust cross-chain functionality and privacy features.
Mayank Arora, M V Gururaj, Ankush Sharma, Naveen Chilamkurti
The transition towards decentralized energy systems has spurred the need for innovative consensus mechanisms to facilitate efficient and transparent energy trading among prosumers. In response to this challenge, we propose a novel Proof of Energy Authentication and Contribution (PoEAC) consensus mechanism tailored for decentralized energy trading systems. PoEAC integrates cryptographic authentication and contribution verification to empower authenticated prosumers in the energy market. Prosumers authenticate themselves by proving ownership of energy-producing assets or storage devices, while demonstrating their contribution to the energy system through verifiable evidence of energy production or storage capacity. Leveraging cryptographic techniques such as zero-knowledge proofs and digital signatures, prosumers generate proofs of their authenticated status and contribution, which are evaluated by the consensus algorithm to validate energy transactions. The proposed model was simulated in MATLAB, with four prosumers over a 24hour horizon. Simulation results confirm that PoEAC successfully validates all legitimate energy transactions while rejecting 100 % of invalid or unauthorized trades. This paper presents the design and implementation of PoEAC, highlighting its advantages in enhancing trust, transparency, and incentivized participation in decentralized energy trading systems.
Quantum pseudorandomness is an emerging research area. Ji, Liu, and Song defined pseudorandom states (PRSs) and pseudorandom unitaries (PRUs) as quantum analogs of pseudorandom generators and pseudorandom functions. A unitary oracle separation result between one-way functions and PRSs/PRUs, established by Kretschmer, suggests that certain quantum primitives may remain secure even if classical cryptography is compromised. This insight has spurred extensive work on quantum pseudorandomness and its applications in quantum cryptography. Many constructions of PRSs have been established under standard assumptions, yet building a secure PRU was a long-standing open problem. This dissertation aims to narrow the gap between PRSs and PRUs and presents results that go beyond PRSs. We introduce Pseudorandom State Scramblers (PRSSs), a new primitive that lies between PRSs and PRUs. A PRSS maps any pure state to a pseudorandom state, a property shared with PRUs but not with PRSs. We present a construction of PRSSs inspired by the well-known Kac’s walk, and in particular, we develop a parallel variant that significantly accelerates the mixing time, enabling an efficient construction. PRSSs support cryptographic tasks not known to be achievable from PRSs alone, including a quantum encryption scheme and a succinct quantum state commitment. Additionally, when suitable classical randomness is provided, our construction exhibits a special dispersing property not known to be satisfied by any existing construction of quantum pseudorandom primitives. Our subsequent work shows that, without asymptotically increasing the number of steps, our construction based on the parallel Kac’s walk yields PRUs with standard or even strong security. The proof builds on a recently developed technique for establishing adaptive security, known as the path-recording method. This result provides an alternative construction of PRUs and further showcases the power of this proof technique. In addition, this dissertation includes two side projects. The first revisits the Hidden Subgroup Problem over ℤn, providing a simplified analysis of a known quantum algorithm using elementary lattice tools. The second establishes a quantum analogue of a classical impossibility result for statistical non-interactive zero-knowledge arguments, showing limitations of black-box reductions under classical-query quantum adversaries.
The growing integration of microservices exacerbates the mounting concern over privacy and trust caused by frequent data leaks and breaches. These systems are inherently decentralized, making them harder to manage, increasing the attack surface, and complicating trust between independently deployed services. Despite these persistent challenges, trust and authentication are often handled using centralized and stateful methods that require storing extensive state information. Existing so-called stateless authentication methods typically shift state storage from the server to the client side rather than detaching from state dependency. To address this challenge, we propose a lightweight, scalable, and truly stateless authentication approach that is non-idempotent and utilizes self-resetting nested Zero-Knowledge Proofs (ZKPs) to eliminate reliance on past actions. This platform anticipates user intentions and rigorously monitors them, ensuring bidirectional operational integrity while preserving privacy. Specifically, the self-resetting ZKP mechanism leverages the dynamic nature of microservices and incorporates random proof segmentation as a random generator to ensure each request is unique. Our tests using the OpenTelemetry Demo confirm its functionality and the independence between requests. Furthermore, security analysis demonstrates comprehensive security through non-idempotent authentication and the uniqueness of proof segmentation for each request.