Federated Learning (FL) has emerged as a distributed paradigm enabling multiple participants to collaboratively train machine learning models without sharing their raw data. By keeping data local, FL mitigates many privacy risks inherent to centralized learning architectures. However, despite this promise, recent research has revealed that exchanged gradients can still leak sensitive information about local datasets. Furthermore, most existing approaches rely on strong and often unrealistic trust assumptions toward the central server, while providing no means to verify whether privacy-preserving mechanisms have been correctly enforced. These limitations expose a critical gap between theoretical privacy guarantees and their practical implementation in real-world federated systems.This thesis investigates how to bridge this gap by combining differential privacy (DP) with cryptographic and verifiability protocols to achieve verifiable and trust-reduced federated learning. First, we explore the use of additive homomorphic encryption to protect client updates and minimize reliance on a trusted aggregator. Second, we introduce a non-interactive verifiability protocol based on zk-SNARKs and cryptographic hashes, allowing third parties to prove and verify the correct application of DP without revealing sensitive information. Finally, we propose ProoFed, a distributed framework that leverages secret sharing to decentralize noise generation and integrate verifiable aggregation proofs in zero knowledge, thereby eliminating single points of trust.
BitBallot: Final Proposal Summary Overview This document presents the final architectural design of BitBallot, an electronic voting system engineered for legally binding public elections under explicit institutional and physical deployment assumptions. Architectural Innovation BitBallot addresses long-standing limitations of end-to-end verifiable voting by separating cryptographic enforcement from institutional responsibility. * Execution Model: Rather than relying on trusted execution environments (TEEs), specialized hardware, or application logic embedded in the consensus layer, the system enforces correctness through verifiable programs (vProgs) and zero-knowledge proofs (ZKP). Infrastructure: It utilizes a public Layer-1 blockchain solely as a neutral substrate for ordering and finality. Core Contribution: Atomic Display Integrity (ADI) The central breakthrough of BitBallot is Atomic Display Integrity (ADI)—a protocol-level security property that: Cryptographically binds voter intent, interface display, and recorded ballots. Ensures a single atomic authorization event at the moment of confirmation. Maintains integrity even under re-voting semantics. Strategic Advantage: Combined with last-vote-valid voting and terminal-complete zero-knowledge tallying, BitBallot achieves strong privacy, public verifiability, and resistance to coercion without intermediate information leakage. Practical Deployment BitBallot is purpose-built for deployment on commodity hardware within supervised polling environments. Cost & Complexity: By avoiding trusted execution environments and specialized cryptographic hardware, the system significantly reduces operational complexity and deployment costs. Security: Despite using standard hardware, it preserves rigorous security guarantees through its underlying protocol design. Conclusion Together, these design choices demonstrate that large-scale, verifiable public elections can be implemented using architectures that are both cryptographically sound and institutionally realistic.
The shortcomings of centralized authentication have led to a move toward decentralized, tamper-resistant solutions as vehicle networks develop. An organized review of blockchain and cryptographic techniques for vehicle authentication and verification is presented in this study. It divides existing approaches into identity management models (decentralized IDs, PKI-less systems), cryptographic techniques (ECC, zero-knowledge proofs, group signatures), consensus mechanisms (PBFT, PoW, DPoS), and hybrid blockchain-IoT frameworks. The analysis examines trade-offs between security, latency, and scalability while presenting a novel taxonomy that matches focused solutions with risks unique to VANETs, like message forgery and Sybil attacks. The increasing use of privacy-preserving authentication techniques and the possibility of post-quantum secure blockchain systems are highlighted. Important insights for boosting resilience and confidence in next vehicle systems are provided by this work.
Abstract This paper revisits Virgo, a well-known transparent zero-knowledge proof system that has been used in many subsequent studies. Through our analysis, we uncover previously overlooked limitations and several exploitable security vulnerabilities within Virgo’s zkVPD protocol design and implementation. We subsequently address these issues and improve Virgo’s zkVPD protocol. Our improvements feature simplified but more efficient VPD and zkVPD algorithms, offering enhanced support for computations over binary fields and their extension fields.
Financial institutions are currently faced with suffering never experienced before as they strive to guarantee the privacy of data and address the demands of regulation to report and cooperate in machine learning. This paper proposes PrivChain-AI, a novel blockchain-based federated learning system designed to facilitate secure and privacy-preserving financial reporting and access control. The proposed framework will integrate three key components: differential privacy, homomorphic encryption, and smart contract-based governance, enabling cooperative model training across financial institutions while preventing the leakage of sensitive information. PrivChain-AI is a hierarchical design that incorporates permissioned consensus protocols and utilises zero-knowledge proof verification to authenticate transactions. It has been demonstrated that the performance is higher than that of the actual financial data, with an outcome of 94.7% accuracy in fraud recognition at the cost of e-differentiation privacy, where ϵ = 1.0. It is 40% faster in terms of communication overhead and ensures regulatory compliance, as it features immutable audit trails. The analysis of performances reveals that a privacy preservation metric improves by 78%, and access control granularity is improved by 62% compared to the current state-of-the-art approaches. The PrivChain-AI paradigm introduced provides a new analytical model for safe, collaborative finance, meeting the highest standards and ensuring compliance with relevant regulatory jurisdictions.
The rapid growth of IoT devices in smart home environments has introduced significant challenges in ensuring secure, scalable, and efficient communication among heterogeneous devices. Centralized architectures suffer from a single point of failure, while blockchain-only solutions face high latency, limiting their use in real-time control. To address these issues, we propose a multi-layered decentralized framework that combines a consortium blockchain, a trusted off-chain coordinator, group-based zero-knowledge proofs (ZKPs), and a two-tiered access control policy (ACP) architecture. The consortium blockchain provides an immutable ledger for device identities and foundational, coarse-grained ACP enforcement through smart contracts, ensuring tamper-proof trust. For privacy-preserving mutual authentication, a group-based ZKP protocol enables collective device authorization without revealing sensitive keys. The off-chain coordinator complements this by enforcing dynamic security mechanisms, including fine-grained ACPv2 checks—such as rate limits, time-of-day restrictions, and device telemetry—in addition to anomaly detection for behavioral risk assessment. This proposed hybrid structure achieves both immutability and high efficiency over traditional methods. A performance evaluation highlighted the framework’s efficiency by demonstrating that the core ZKP verification for a 500-device group can be completed in just 190 ms. The framework drastically reduces on-chain costs, with critical access control policy transactions consuming only 82,748 gas—a reduction of over 90% compared to benchmarked on-chain systems. The complete end-to-end workflow, from user request to secure session establishment, has a latency bound of approximately 3s. Formal security verification with the BAN and AVISPA tools validates resilience against common attacks, including man-in-the-middle, replay, and impersonation, while static analysis using the Slither framework confirms the absence of critical vulnerabilities in the smart contract code. By combining an immutable on-chain foundation with intelligent, dynamic off-chain enforcement, our proposed framework provides a uniquely resilient, scalable, and adaptive security solution for modern smart home systems.
Damilare E. Bakare, Adekemi Olawunmi Amoo, Mary T. Onifade
The health insurance sector has been facing many challenges recently, such as fraudulent activities in insurance claims, data breaches, and high transaction costs, particularly with existing systems built on the Ethereum network, which negatively affect its efficiency and effectiveness.These challenges undermine the trust and financials of insurance providers while compromising the privacy of the patient's health records.To address this issue, this study proposes a conceptual framework that uses zero-knowledge proof within the blockchain system and is deployed on the Polygon Network for its low transaction fees and higher throughput.The proposed model allows the verification of an insurance claim without revealing sensitive patient health records, ensuring privacy while preventing fraudulent activities.In this conceptual design, the hospital can issue verifiable proof of treatment, appointment, and bill that shows the validity of the insurance claim without revealing the underlying health record to the insurer.This study, therefore, contributes to supporting research in decentralized applications for healthcare insurance by presenting a conceptual model and comprehensively analyzing the feasibility, rather than a full-scale implementation.It also emphasizes the need to preserve privacy in sensitive domains and the potential benefits of blockchain and ZKP integration.In conclusion, the research's findings show that, in theory, integrating ZKP with blockchain technology can enhance healthcare insurance processes in terms of reliability, efficiency, privacy, and security.However, further research and practical development are required to realize and evaluate a fully operational system.
Digital product passports outline information about a product’s lifecycle, circularity, and sustainability-related data. Sustainability data contains claims about carbon footprint, recycled material composition, ethical sourcing of production materials, etc. Also, upcoming regulatory directives require companies to disclose this type of information. However, current sustainability reporting practices face challenges, such as greenwashing, where companies make incorrect claims that are difficult to verify. There is also a challenge of disclosing sensitive production information when other stakeholders, such as consumers or other economic operators, wish to verify sustainability claims independently. Zero-knowledge proofs (ZKPs) provide a cryptographic system for verifying statements without revealing sensitive information. The goal of this research paper is to explore ZKP cryptography, trust models, and implementation concepts for extending DPP capability in privacy-aware reporting and verification of sustainability claims in products. To achieve this goal, first, formal representations of sustainability claims are provided. Then, a data matrix and trust model for generating proofs are developed. An interaction sequence is provided to show different components for various proof generation and verification scenarios for sustainability claims. Lastly, the paper provides a circuit template for the proof generation of an example claim and a credential structure for their input data validation. The proposed approach is assessed using a scenario-based evaluation to check the performance metrics for data credential verification and proof generation for verifying material composition in a product.
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Physical Unclonable Functions (PUFs) and Hardware Security
An open question recently posed by Fawzi and Ferme [IEEE Transactions on Information Theory 2024], asks whether non-signaling (NS) assistance can increase the capacity of a broadcast channel (BC). We answer this question in the affirmative, by showing that for a certainK-receiver BC model, called Coordinated Multipoint broadcast (CoMP BC) that arises naturally in wireless networks, NS-assistance provides multiplicative gains in both capacity and degrees of freedom (DoF), even achievingK-fold improvements in extremal cases. Somewhat surprisingly, this is shown to be true even for 2-receiver broadcast channels that are semi-deterministic and/or degraded. In a CoMP BC,Bsingle-antenna transmitters, supported by a backhaul that allows them to share data, act as oneB-antenna transmitter, to send independent messages toKreceivers, each equipped with a single receive antenna. A fixed and globally known connectivity matrix specifies for each transmit antenna, the subset of receivers that are connected to (have a non-zero channel coefficient to) that antenna. Besides the connectivity, there is no channel state information at the transmitter. The receivers have perfect channel knowledge. We show that NS-assistance has no DoF advantage in a fully connected CoMP BC. The DoF region is fully characterized for a class of connectivity patterns associated with tree graphs, for which the classical sum-DoF value is shown to be the number of leaf nodes, while the NS-assisted sum-DoF value is the total number of all (non-root) nodes. For arbitrary connectivity patterns, the sum-capacity with NS-assistance is bounded above and below by the min-rank and triangle number of the connectivity matrix, respectively, leading to matching bounds in many cases, e.g., if min(B,K) ≤ 6. While translations to Gaussian settings are demonstrated, for simplicity most of our results are presented under noise-free, finite-field (Fq) models. Converse proofs for classical DoF are found by adapting the Aligned Images bounds to the finite field model. Converse bounds for NS-assisted DoF/capacity extend the same-marginals property to the BC with NS-assistance available to all parties. Beyond the BC setting, even stronger (unbounded) gains in capacity due to NS-assistance are established for certain ‘communication with side-information’ settings, such as the fading dirty paper channel.
Jitao Wang, Nong Tang, Yuzhou Wang, Kai Wang · 5 authors
Cross-chain technology, as a key driver for enhancing interoperability of blockchains, enables asset transfer and exchange between different blockchains. At present, cross-chain models based on light clients are widely adopted due to their fully decentralized nature and applicability to diverse scenarios. However, the rapid advancement of on-chain analysis techniques, such as address linkage and fund flow tracking, has significantly increased risks of de-anonymization in cross-chain transactions, posing serious privacy challenges. In this paper, we propose SharedRXC, a privacy-preserving asset cross-chain scheme for the light-client cross-chain model, which guarantees address unlinkability without extra privacy trust assumptions. First, to hide cross-chain addresses during interchain transmission, we propose the Ring Account (RA) to replace a single address for sending or receiving funds. In addition, we propose a zero-knowledge proof-based method to verify virtual identity ownership, allowing the virtual identity to track fund balances without exposing the addresses. Second, to prevent the exposure of the link between an address and its virtual identity caused by fund amount differences during deposits or withdrawals, which would compromise address unlinkability, we propose the Shared Burn/Mint method to obscure on chain fund change differences. Based on the liability equalization mechanism, we design two privacy-preserving cross-chain protocols: the cross-chain asset transfer (SharedRXC.T) and exchange (SharedRXC.E) protocols. Finally, we evaluateSharedRXC.TandSharedRXC.E, which reduce gas costs by 30% to 40% compared to zkCross and achieve execution times in the millisecond range. Therefore, SharedRXC provides a practical privacy-preserving solution for cross-chain financial applications in the multi-chain ecosystem.
Dhanush K, Tamilvelan S, Hari L, R. Roopa Chandrika
The growing adoption of lightweight, scalable, and resistant to tampering security mechanisms in the face of broad use of Internet-of-Things (IoT) and edge computing devices requires such mechanisms to be not based on centralized trust or bulky cryptography. The promising answer to this is the concept of Physical Unclonable Functions (PUFs) which relies on naturally existing manufacturing differences to produce device-specific, unclonable responses. Nevertheless, existing PUF-based authentication systems have significant flaws, such as centralized Challenge-Response Pair (CRP) storage vulnerable to attacks, vulnerability to machine learning, and no support of secure data recovery and sharing in distributed settings. This paper suggests a distributed authentication and recoverable data sharing framework that reduces these drawbacks, PUF-as-a-Service (PUFaaS). PUFaaS presents a multi-dimensional space of CRP, spreading the domain of challenges with respect to various operational parameters to maximize security against modeling attacks. Helper data of fuzzy extractors are secured with Shamir's secret sharing over distributed nodes and without having single points of weakness. A mechanism based on fuzzy vaults provides recoverable data binding, whereby encryption keys or sensitive data can be re-assembled successfully on successful verification of stable PUF responses. Authentication is carried out by way of lightweight commitment protocol, message authentication protocol and optional zero-knowledge proof guarantees privacy. Experimental analysis shows that PUFaaS can be evaluated as having low false acceptance and rejection, high modeling attack resistance, and scale efficiently (appropriate to large-scale IoT and cloud-edge). PUFaaS will offer an effective, privacy-resilient, and scalable solution to distributed authentication and secure information exchange in a non-trusted environment by converting PUFs into a service-oriented architecture.
Physical Unclonable Functions (PUFs) and Hardware Security
This paper presents zero knowledge proofs, their cryptographic significance and applications. It presents a basic classification: interactive and noninteractive zero knowledge proofs. It presents and compares three protocols of non-interactive zero knowledge proofs: ZK-SNARK, ZK-STARK and Bulletproofs. It presents the quadratic residue problem and proofs it with both interactive and non-interactive zero knowledge proofs. The non-interactive protocol used to prove the quadratic residue problem is ZK-SNARK. The proof is implemented in the Python programming language, using python-snark library.
Identity management is a critical component in egovernance, ensuring secure, reliable, and efficient verification of citizens' identities. With increasing digitization, protecting personal data while enabling seamless access to government services has become essential. Existing identity management systems often rely on centralized databases, which are prone to data breaches, unauthorized access, and lack of transparency, raising concerns over privacy and trust. To address these challenges, this research proposes a Blockchain Identity Framework that integrates Zero-Knowledge Proof (BIF-ZKP) authentication with blockchain consensus mechanisms. In this framework, ZKP enables users to prove their identity without revealing sensitive information, while blockchain ensures that identity records are decentralized, tamperproof, and auditable. The consensus mechanism guarantees that all identity transactions are verified by multiple nodes, reducing the risk of fraud and unauthorized modifications. The proposed method is applied in an e-governance context to securely manage citizens' digital identities, enabling authentication for services such as online voting, tax filings, and social welfare schemes while maintaining privacy. Experimental evaluation demonstrates that the BIF significantly enhances data security, privacy preservation, and trustworthiness compared to traditional centralized identity systems. It reduces the risk of identity fraud and ensures the verifiable and transparent management of citizens' information. The proposed method improves data security by 96.2 % and reduces fraud by 89 %.
ABSTRACT Phishing attacks in decentralized Web3 systems continue to evolve beyond the detection capabilities of traditional Web2 security models. Existing decentralized authentication systems typically lack either mutual verification or dynamic threat awareness. We present PhishGuard++, a cross‐chain, privacy‐preserving authentication framework that introduces two core innovations: (1) a novel mutual Zero‐Knowledge Proof (ZKP) protocol that validates both users and services using Decentralized Identifiers (DIDs), and (2) a real‐time, on‐chain Graph Neural Network (GNN) threat oracle that assigns phishing risk scores integrated directly into smart contract‐based access control logic. A stake‐based validator reputation system with anti‐collusion incentives further reinforces trust without sacrificing decentralization or privacy. Experimental results on a simulated Arbitrum testnet show a statistically significant 40.4% reduction in phishing success rate across five attack vectors, 98.6% authentication accuracy, and sub‐second latency with gas‐efficient operations. Unlike prior works that independently apply ZKPs, DIDs, or GNNs, this framework offers the first privacy‐preserving, mutual authentication system that combines these technologies with stake‐based economic enforcement and real‐time smart contract enforcement. The novelty lies in the architecture's real‐time threat‐aware access decisions, validator‐linked risk accountability, and practical cross‐chain deployment—an integration not previously achieved.
Ghassan Samara, Ibrahim Obeidat, Mais Haj Qasem, Raed Alazaidah · 9 authors
Vehicular networks must authenticate high-rate safety messages under tight latency while preserving privacy and remaining secure against post-quantum adversaries. We present$P Q$-Rate, a post-quantum, privacy-preserving, rate-limited credential system for C-V2X. PQ-Rate replaces per-message signatures with a one-round-trip (1-RTT) KEM handshake to derive an AEAD session key, keeping the fast path lightweight. Vehicles obtain unlinkable Rate-Limited Anonymous Tokens (RLATs) from edge issuers (RSU/MEC) via a threshold VOPRF; each token is spent once using a zero-knowledge proof that binds a unique nullifier, enforcing per-epoch budgets without revealing identity. Revocation scales via compact, signed Bloom-filter digests broadcast by RSUs, with optional online disambiguation to handle rare false positives, and accountability is provided by threshold opening of verifiably encrypted token metadata. We prototype PQ-Rate in NS-3 with SUMO-driven mobility and compare against pseudonym-based PKI and verifier-local-revocation group signatures. Metrics include 1-RTT authentication latency, beacon delivery ratio (PDR), channel busy ratio (CBR), verifier throughput, revocation-wire overhead, and realized Sybil capacity. Results show that PQ-Rate maintains sub- 20 ms session setup, improves PDR under high density by reducing airtime overhead, increases verifier throughput via batching and inexpensive decapsulation, and bounds attacker identities to the product of enrolled hardware modules and budget. Revocation digests remain small (on the order of 9-12 KB for thousands of inserts at$10^{-3}-10^{-4}$false-positive targets), supporting frequent broadcast without inflating CBR. PQ-Rate demonstrates that post-quantum security, strong privacy, Sybil resistance, and rapid revocation can be achieved simultaneously within V2X timing constraints.
Cryptographic Implementations and Security
Physical Unclonable Functions (PUFs) and Hardware Security
With the rapid development of geological blockchains and Internet of Things-based data acquisition technologies, massive amounts of heterogeneous data are constantly emerging. However, this data is stored in a distributed manner across different organizational or business blockchains. Data sharing among multiple geological blockchains faces numerous challenges, either exposing sensitive data during verification or lacking effective authorization mechanisms. Therefore, how to achieve fine-grained access control and privacy protection across multiple blockchains has become a critical issue that must be addressed in geological data sharing. In this paper, we propose GeoCross, a cross-chain geological data sharing framework that enables fine-grained authorization management and privacy protection. First, GeoCross provides a hierarchical hybrid encryption mechanism that uses symmetric encryption for geological data protection and ciphertext-policy attribute-based encryption to enable flexible cross-chain access policies. Second, we integrate a Groth16-based zero-knowledge proof mechanism, which allows a chain to verify the existence, integrity, and accessibility of off-chain data without revealing the content. Furthermore, we introduce a Reputation-based Non-interactive Relay node Selection protocol (RNRS), which enhances the trustworthiness and fairness of cross-chain routing. Finally, we implement GeoCross in a multi-chain Hyperledger Fabric environment and evaluate its performance under real-world workloads. Results show that Groth16 verification requires only three bilinear pairings, achieving a throughput of up to 390 tps on a single chain and 1550 tps in a concurrent multi-chain environment. Even with 50% malicious nodes, the RNRS protocol still maintains a success rate of over 91%. These results demonstrate that GeoCross provides an efficient and practical solution for secure and privacy-preserving cross-chain geological data sharing.
Secure electronic voting (e-voting) systems have become an essential component of modern democratic processes, demanding strong guarantees of privacy, integrity, verifiability, and resistance to coercion. Homomorphic commitment schemes, which integrate the properties of commitment schemes with homomorphic encryption, provide a promising approach to meeting these requirements by enabling computations on encrypted or committed data without revealing the underlying information. This capability allows secure vote tallying while preserving voter anonymity. This paper presents a comprehensive review of homomorphic commitment schemes within secure voting infrastructures, focusing on key security models such as privacy, verifiability, coercion resistance, and robustness against malicious adversaries. It also examines optimization techniques, including batching, threshold cryptography, and blockchain integration, which enhance system efficiency and scalability. Furthermore, emerging paradigms such as post-quantum cryptography and decentralized systems are discussed for their potential impact on voting protocols. The study highlights advancements in cryptographic primitives, zero-knowledge proofs, and distributed ledger technologies, while providing a comparative analysis of multiple research contributions. The findings indicate that although homomorphic commitment schemes significantly enhance transparency and privacy, challenges related to computational complexity, scalability, and real-world implementation persist, suggesting the need for lightweight, quantum-resistant, and hybrid secure voting solutions. , , , ,
Blockchain and artificial intelligence (AI) are reshaping the financial landscape by improving security, operational efficiency, and intelligent automation. Blockchain&s;s decentralized and tamper-proof ledger fosters transparency and trust in financial transactions, while AI enhances decision-making through advanced data analysis, fraud detection, and risk management. Together, their convergence supports a wide range of applications, including decentralized finance (DeFi), asset tokenization, algorithmic trading, and robo-advisory services. Blockchain provides a secure infrastructure for AI-driven financial innovations by ensuring data integrity and minimizing dependence on intermediaries. In parallel, AI improves blockchain performance by automating smart contracts, refining predictive models, and streamlining compliance mechanisms. To better understand and structure this integration, the chapter introduces the Techno-Financial Synergy Framework (TFSF), which connects the technological enablers, strategic drivers, and outcomes of AI-blockchain convergence. While the synergy holds great promise, it is also accompanied by challenges such as computational overhead, scalability constraints, interoperability gaps, and evolving regulatory landscapes. However, emerging solutions such as zero-knowledge proofs, homomorphic encryption, and next-generation consensus protocols are gradually addressing these limitations. As these technologies continue to evolve, their convergence is expected to drive the next phase of digital financial transformation, fostering a smarter, more secure, and inclusive financial ecosystem.
P. Chinnasamy, R. Shashidhar Reddy, Y. Lohith Kiran, D. Prathap Reddy · 5 authors
In the age of using technology to work together remotely, the inability to share files in a secure manner is a problem often faced since many online sharing options available do not have good protection on their sharing options and do not safeguard against unauthorized use of the files. This paper presents a secure file sharing portal that has end to end encryption and user-based access controls and that works inside a web browser. The system uses the Web Crypto API interface to provide local encryption on the user’s devices using the AES-GCM encryption algorithm, so that the user’s plaintext documents do not leave the device. A separate layer of protection exists in the system. It is not enough for a user to just receive the encrypted file. The user has to receive a decryption key that the sender has to share through a separate channel. Access is for members only which requires sender approval and we have features like secure QR code share, session monitoring, digital certificates for identity proof and policy enforcement for compliance. Also, we have a "Secure Space" module which is for ephemeral work groups that has in space chat, multi user invites, controlled key exchange and one click revocation which in turn puts power back in the users’ hands. We combined zero knowledge structure with audit able workflows to present a privacy first, scalable and easy to use solution. Also, we show how we used modern web tech to create a trusted setting for sensitive file share which at the same time does not sacrifice ease of use or performance.
High-mobility Internet of Things (IoT) for Vehicle-to-Grid (V2G) Demand Response (DR), including roaming between Charge Point Operators (CPOs), requires privacy-preserving authentication with sub-1 ms responses and cross-domain scalability as devices exceed 200km/h. Mechanisms must run on constrained hardware while remaining compatible with EV-charging message flows such as ISO 15118–20 and OCPP 2.0.1. Many deployed schemes re-authenticate from scratch, which inflates computation and airtime; static credentials also ignore trajectory context and struggle with rapid mobility. We present a Zero-Knowledge Proof-based Authentication Scheme (ZKPAS) for V2G/DR that proves possession without disclosure and replaces heavy handshakes with compact, mobility-aware proofs, targeting latencyLO(n) toO(logn). (iii) Predictive token generation with Long Short-Term Memory (LSTM) models trained on GeoLife and T-Drive pre-computes material, yielding 84.7% token reuse along trajectories. (iv) Cross-domain authentication employs (t,n)-threshold cryptography for Byzantine-tolerant roaming across operators. We prove resistance to impersonation, replay, man-in-the-middle, and trajectory inference; under the Computational Diffie–Hellman Problem (CDHP), the adversary’s success probability satisfies Pr[break] ≤ 2−λ. On real transportation topologies, ZKPAS cuts computation by 71.8%, authentication latency by 93.9%, and energy by 69.5%, while interfacing with V2G/DR control flows. The protocol sustains a 98.5% authentication success rate at 250km/h.
We study non-interactive zero-knowledge proofs (NIZKs) for NP satisfying: 1) statistical soundness, 2) computational zero-knowledge and 3) certified-everlasting zero-knowledge (CE-ZK). The CE-ZK property allows a verifier of a quantum proof to revoke the proof in a way that can be checked (certified) by the prover. Conditioned on successful certification, the verifier's state can be efficiently simulated with only the statement, in a statistically indistinguishable way. Our contributions regarding these certified-everlasting NIZKs (CE-NIZKs) are as follows: - We identify a barrier to obtaining CE-NIZKs in the CRS model via generalizations of known interactive zero-knowledge proofs that satisfy CE-ZK. - We circumvent this by constructing CE-NIZK from black-box use of NIZK for NP satisfying certain properties, along with OWFs. As a result, we obtain CE-NIZKs for NP in the CRS model, based on polynomial hardness of the learning with errors (LWE) assumption. - In addition, we observe that the aforementioned barrier does not apply to the shared EPR model. We leverage this fact to construct a CE-NIZK for NP in this model based on any statistical binding hidden-bits generator, which can be based on LWE. The only quantum computation in this protocol involves single-qubit measurements of the shared EPR pairs.
We present the Y.I.N. Mazari Architecture, an 8-pillar privacy-preserving federated learning system built around a novel cryptographic ordering: DP→ZK→HE (Differential Privacy →Zero-Knowledge Proof →Homomorphic Encryption) applied to federated learning gradients. The name Y.I.N. honors Yanis, Ilyan, and Neylia Mazari, while embodying the core principle that Your Information Never leaves your control.We identify a fundamental barrier in privacy-preserving federated learning: the inability to verify that participants correctly applied differential privacy noise while maintainin computational efficiency. The Y.I.N. Mazari Ordering resolves this barrier through a specific sequencing of cryptographic operations.This paper extends the classical architecture into the quantum domain through the QFED-MAZARI system,introducing the Mazari Quantum Ordering: QDP→MUA→DQEM(Quantum Differential Privacy →Manifold Unitary Aggregation →Distributed Quantum Error Mitigation). Experimental results demonstrate 99.37% model accuracy with 223× speed improvement in classical systems, while the quantum extension achieves 91.9% accuracy with 40–50% communication reduction. Together, the classical and quantum architectures establish a comprehensive 30-year intellectual property runway.
Electronic voting is crucial for contemporary democratic processes. However, conventional systems often struggle with a single point of failure and insufficient support for intricate voting semantics, especially in ranked-choice elections requiring distinct ranking constraints. Although blockchain technology enhances fault tolerance and auditability, existing blockchainbased solutions primarily focus on simpler voting schemes, encountering difficulties in effectively verifying privacy-preserving ranked ballots. To address this gap, we propose AranVoting, an innovative blockchain-based ranked-choice voting scheme designed to ensure anonymity and fairness through homomorphic encryption. AranVoting employs a structured matrix ballot format alongside zero-knowledge proof to ensure the correctness of the ballot format. Furthermore, we introduce a smart contractdriven counting mechanism that facilitates the availability and transparency of ballot tallying through gradient incentive and committee election algorithms. Our security and performance evaluations demonstrate that AranVoting provides secure rankedchoice voting, effectively guaranteeing essential security properties such as anonymity, correctness, and verifiability while maintaining practicality and reasonable computational overhead throughout the voting process.