Traditional zero trust architectures (ZTA) rely on centralized policy engines and mutable audit logs, creating single points of failure and limiting forensic integrity. We present a novel blockchain-secured zero trust network architecture that integrates distributed ledger technology, machine learning-based threat detection, and zero-knowledge proof authentication to eliminate the se limitations. Our five-layer framework comprises a user access layer, a zero-trust core (policy engine, administrator, and enforcement points), a blockchain security layer (smart con- tracts, consensus engine, and audit trail), a decentralized identity layer (W3CDIDs and zero-knowledge proof authentication), and protected network resources. Access requests are validated via zero-knowledge proofs; trust scores are computed dynamically using Random Forest classifiers achieving 96.3% accuracy; policy decisions are executed through Practical Byzantine Fault Tolerance (PBFT) consensus; and all security events are recorded immutably on Hyperledger Fabric. Smart contracts create automated incident response, which isolates malicious parties in less than 500 ms without human interference. Experiments on a 50-node testbed during simulated attacks show an authentication latency of < 100 ms, a throughput of > 1,000transactions per second (TPS), a threat-detection accuracy of > 95%, and a false-positive rate of < 5%. Our solution reduces the latency by 30% and the operating overhead by 60% and offers 100% integrity of the audit trail compared to traditional zero-trust implementations. This publication represents the first end-to-end integration of blockchain and zero-trust systems, providing a privacy-preserving, scalable, and resilient security architecture for enterprise environments and next-generation networks.
Existing agent communication frameworks face critical limitations in providing verifiable audit trails without compromising the privacy and confidentiality of agent interactions. The protection of agent communication privacy while ensuring auditability emerges as a fundamental challenge for applications requiring accurate billing, compliance verification, and accountability in regulated environments. We introduce a framework for auditing agent communications that keeps messages private while still checking they follow expected rules. It pairs zero-knowledge proofs with the existing Model Context Protocol (MCP) so messages can be verified without revealing their contents. The approach runs in lightweight networks, stays compatible with standard MCP exchanges, and adds asynchronous audit verification to confirm format and general message types without exposing specifics. The framework enables mutual audits between agents: one side can check communication content and quality while the other verifies usage metrics, all without revealing sensitive information. We formalize security goals and show that zk-MCP provides data authenticity and communication privacy, achieving efficient verification with negligible latency overhead. We fully implement the framework, including Circom-based zero-knowledge proof generation and an audit protocol integrated with MCP's bidirectional channel, and, to our knowledge, this is the first privacy-preserving audit system for agent communications that offers verifiable mutual auditing without exposing message content or compromising agent privacy.
High-value payment transactions (HVTs) face heightened exposure to money laundering risks due to their large monetary volumes, cross-jurisdictional nature, and the increasing complexity of financial networks. Traditional Anti-Money Laundering (AML) procedures rely heavily on sharing customer identities, transactional attributes, and risk-model outputs across institutions and regulators—creating substantial privacy, security, and data-handling risks. Zero-Knowledge Proofs (ZKPs) offer a transformative alternative by enabling financial institutions to prove compliance with AML requirements without revealing the underlying sensitive information. This paper examines the design and application of ZKP-based compliance frameworks for HVT ecosystems, detailing how AML checks—including KYC verification, sanctions screening, transaction-amount threshold validation, behavioral-risk scoring, and source-of-funds assessment—can be cryptographically attested through privacy-preserving proofs. We propose a hybrid architecture that combines off-chain AML computation with an on-chain ZKP verification and audit layer supported by secure regulatory nodes. Through structured workflows and proof types such as range proofs, list membership proofs, and rule-compliance circuits, the model ensures regulatory oversight while maintaining strict confidentiality. The study also evaluates the performance implications of ZKP systems in high-volume transaction environments and addresses security, interoperability, and oracle-reliability concerns. Ultimately, ZKP-enabled AML frameworks demonstrate significant potential to enhance compliance efficiency, reduce data-exposure risk, and strengthen trust across global payment networks. The paper concludes by outlining future research opportunities, including AI-driven AML circuits, cross-border ZKP interoperability standards, and integration with decentralized identity solutions.
The widespread deployment of Internet of Things (IoT) devices has driven their segmentation into distinct trust domains for the purpose of governance, creating a critical need for secure cross-domain authentication (CDA). CDA must preserve both anonymity and traceability of device identities to enable trustworthy data exchange. However, existing approaches, while exploring this trade-off, remain vulnerable to single points of failure and Sybil attacks—threats that are especially severe for unattended and resource-constrained devices. In this paper, we propose a Self-Sovereign and Supervised Cross-domain authentication scheme (SCross) to tackle these issues. The main building block we designed is a pseudonym management scheme (PMS) that allows devices to generate and use pseudonyms without relying on a trusted party. Although devices has full control of their identities, PMS still ensures traceability, Sybil resistance, and revocability. We define the formal security models of PMS, instantiate it under two different approaches, namely group signature (SCross-GS) and zero-knowledge succinct non-interactive arguments of knowledge (zkSNARKs, SCross-ZK), and present security proofs for our proposal. We implemented and evaluated SCross. The result shows that our scheme achieves an effective trade-off between security and efficiency.
Arthur Ramos, Anjolina Grisi de Oliveira, Ruy de Queiroz, Tiago M. L. de Veras
We present Metatheory, a comprehensive library for programming language foundations in Lean 4. The library features a modular framework for proving confluence of abstract rewriting systems using three classical proof techniques: the diamond property, Newmans lemma, and the Hindley-Rosen lemma. These are instantiated across six case studies including untyped lambda calculus, combinatory logic, term rewriting, simply typed lambda calculus, and STLC with products and sums. All theorems are fully mechanized with zero axioms or sorry statements. We provide complete proofs of de Bruijn substitution infrastructure and demonstrate strong normalization via logical relations. To our knowledge, this is the first comprehensive confluence and normalization framework for Lean 4.
In the field of artificial intelligence, secure model inference is essential for protecting data confidentiality, which allows users to interact with trained models for decision-making support without privacy leakage. However, current secure inference methods often overlook the simultaneous verification of data origins for both user inputs and model weights, which is crucial for maintaining the integrity of inference outcomes. In this study, we present a novel verifiable secure inference scheme that leverages blockchain to enhance the verifiability of both the inference process and the origins of user inputs and model weights. We integrate the decentralized ledger to store the committed inputs and weights, serving as convincing data origins. We then transform neural networks into zero-knowledge proof constraints with optimized structures for the inference process. To illustrate its application scenario, we propose a regulatable privacy-preserving transaction scheme. Its regulation depends on anomaly detection on private transactions without privacy leakage, which takes the encrypted ledger as the data source and the committed detection model as the parameter source to perform our verifiable secure inference. We provide rigorous security proofs for our schemes, demonstrating their authenticity and privacy. We implement them to demonstrate their scalability through analyzing their computational and communication performance.
Blockchain is an emerging technology with a core emphasis on decentralization. Along with this, blockchain is immutable and follows a consensus, thereby promoting data ownership and data provenance to its users. The concept of self-sovereign identity further promotes a decentralized and distributed network. The use cases of blockchain extend beyond the cryptocurrency world. Using decentralized identifiers, smart contracts, and zero-knowledge proofs, verifiable credential models can enhance multiple areas of society. This paper focuses on establishing a communication channel between fishermen and customers within India's fisheries sector, leveraging blockchain technology. The system is designed to be fully traceable and trackable, ensuring transparency. Fishermen retain ownership of their data, empowering them to receive fair prices, while consumers gain confidence in the authenticity of their purchases. To support trust, a data-driven layer analyzes state-wise valuation and pricing trends from 2020 to 2023. These insights help verified fishermen and buyers make informed decisions with greater transparency and regional context.
Zero-knowledge proofs (ZKPs) are central to secure and privacy-preserving computation, with zk-SNARKs and zk-STARKs emerging as leading frameworks offering distinct trade-offs in efficiency, scalability, and trust assumptions. While their theoretical foundations are well studied, practical performance under real-world conditions remains less understood. In this work, we present a systematic, implementation-level comparison of zk-SNARKs (Groth16) and zk-STARKs using publicly available reference implementations on a consumer-grade ARM platform. Our empirical evaluation covers proof generation time, verification latency, proof size, and CPU profiling. Results show that zk-SNARKs generate proofs 68x faster with 123x smaller proof size, but verify slower and require trusted setup, whereas zk-STARKs, despite larger proofs and slower generation, verify faster and remain transparent and post-quantum secure. Profiling further identifies distinct computational bottlenecks across the two systems, underscoring how execution models and implementation details significantly affect real-world performance. These findings provide actionable insights for developers, protocol designers, and researchers in selecting and optimizing proof systems for applications such as privacy-preserving transactions, verifiable computation, and scalable rollups.
Cloud Computing (CC) is an excellent platform that is widely used to share information as well as services between various departments, customers, and other parties. However, during the transmission of sensitive data, effective security management is essential to ensure data privacy by avoiding unauthorised access. To address this, an efficient key agreement protocol, which is a combination of Elliptic Curve Cryptography with Zero Knowledge Proof (ECC-ZKP) model, is proposed to enhance security management in a cloud environment. The ECC-based encryption model provides equivalent security with much smaller key sizes compared to traditional cryptography approaches. Moreover, this lightweight model reduces computational load and speeds up operations in large-scale cloud environments. The integration of the ZKP model allows users to prove their identity and get access without disclosing any sensitive credentials, which leads to an increase the data confidentiality. Initially, the login and registration of users in the cloud is done by submitting all their details to the service provider. Then, a hash value and bound values are computed, and a secret key is generated by the service provider, which is sent to the client for accessing the data from the cloud. The experiment results of the proposed ECC-ZKP model achieved Makespan of 4.8 ms for 100 tasks, which is better than traditional key agreement models, such as ECC-based authenticated key agreement, respectively.
TetraKlein is a unified computational, cryptographic, and extended-reality (XR) architecture developed by Baramay Station Research Inc., a Canadian non-profit research organization.This repository publishes the mathematical framework, AIR constraint system, XR physics formulations, digital-twin convergence rules, and verification pipeline defining the TetraKlein system. TetraKlein integrates: Post-quantum cryptography (Kyber, Dilithium, Module-LWE/SIS) Zero-knowledge proof systems (AIR, STARKs, IVC, folding, FRI) Verifiable compute engines (SP1, RISC Zero, Brevis, zkSync-derived provers) XR physics and rendering pipelines (TK-U, XR-TSU kernels, foveation models) Digital-Twin Convergence (DTC lineage, projection operators) Hypercube ledger topology (HBB, Recursive Tesseract Hashing) IPv6-native mesh identity and routing (Yggdrasil, PQC-authenticated overlays) Cross-layer AIR constraints enabling recursive multi-domain verification This repository is intended for researchers, engineers, cryptographers, XR developers, and academic institutions looking to analyze, extend, or experimentally validate a unified verifiable-compute architecture. Research-Stage Disclaimer The current version of the TetraKlein architecture is an early-stage, research-oriented framework. It is not a production system and makes no claims of operational readiness, security guarantees, clinical or industrial safety, or real-world deployment feasibility. All mathematical models, AIR constraints, XR physics bindings, digital-twin operators, and ledger constructs are subject to heavy scrutiny, independent validation, and long-term peer review. The material in this repository should be treated strictly as a research roadmap—a foundation for future work that will require extensive testing, reproducibility studies, formal verification, adversarial analysis, and multi-year refinement by the broader scientific and engineering community before any practical use is considered. Key Capabilities 1. Deterministic TK-VM Execution Layer The TetraKlein Virtual Machine provides a deterministic, low-degree constrained execution environment: XR frame physics evolution pose + camera kinematics TSU-compatible energy constraints DTC projection hypercube-ledger synchronization ZK-friendly opcode semantics verifiable state transitions All TK-VM semantics map directly into algebraic AIR constraints. 2. End-to-End Zero-Knowledge Verification TetraKlein supports: AIR-constrained STARK proving recursive IVC frame folding multi-epoch ledger commitment verifiable rendering pipeline deterministic cross-domain proofs (XR → DTC → Ledger) Every XR frame, physics update, and identity transition is provable. 3. Post-Quantum Identity & Routing Identity and routing combine: Kyber-1024 key-encapsulation Dilithium-V signatures MLWE/SIS identity kernels Yggdrasil IPv6 self-authenticating mesh PQC-bound routing and handshake protocol hypercube-coordinate ledger addressing 4. Hypercube Blockchain Base (HBB) The ledger uses a hypercube topology: adjacency enforced by AIR constraints spectral operators (E1–E4) Recursive Tesseract Hashing multi-epoch finality and spectral stability provable routing correctness deterministic fragment propagation 5. Digital Twin Convergence (DTC) The digital-twin framework provides: XR → DTC projection operator inverse-projection (Ledger → DTC → XR) multi-agent DTC coupling Lyapunov-style stability envelopes convergence-time bounding real-world sensor model coupling (non-invasive) Licensing TetraKlein adopts a dual-license structure: Scientific Content Creative Commons Attribution 4.0 (CC-BY-4.0)All mathematical material, papers, equations, AIR tables, and technical documentation. Software MIT License (simple, permissive)Apache License 2.0 (patent-safe, industry standard) This ensures maximum compatibility with: Ethereum / zkSync RISC Zero / SP1 StarkWare-style STARK ecosystems academic reproduction open-source research About the Original 2025 Manuscript This repository includes the full, unmodified original paper: “TetraKlein: A Post-Quantum, Zero-Knowledge, Multidimensional Cryptographic Network for Mid–21st Century Civilization Infrastructure”Michael Tass MacDonald — November 22, 2025 This archival version is included as-is for historical reference.It may contain: speculative or unverified early-stage ideas preliminary constructions non-peer-reviewed material conceptual frameworks later replaced or refined The unified monograph supersedes this original document. Roadmap Phase 1 — Public Monograph Release (Completed) Unified 1,600+ page architecture TK-VM execution model AIR tables (TK-U … TK-Z) PQC identity system XR physics + DTC Hypercube ledger + RTH Recursive folding pipeline (TK-Y / TK-Z) Phase 2 — Reference Implementation (In Progress) minimal TK-VM interpreter TK-W ledger sponge (Poseidon-style) hypercube router (TK-V) proving-fragment diffusion (TK-X) SP1 / RISC-Zero test harness Phase 3 — XR/DTC Prototype (2026) OpenXR prototyping XR-physics frame pipeline real-time DTC–ledger synchronization Phase 4 — Formal Verification (2026–2027) Coq/Isabelle/HOL formalization independent reproducibility testing security analysis academic peer review and conference submissions Citing This Work A full permanent Zenodo DOI will be provided Citation MacDonald, M. T. (2025). TetraKlein: A Unified Architecture. Baramay Station Research Inc. Public Edition. CC-BY-4.0 / MIT / Apache-2.0. Contact Baramay Station Research Inc.Canadian Non-Profit R&D (Saskatchewan)Director & Principal Investigator: Michael Tass MacDonald Contact michael@baramaystationresearchinc.ca Mission TetraKlein aims to advance: open, verifiable computation transparent scientific methodology reproducible XR and digital-twin research post-quantum cryptography zero-knowledge trust frameworks decentralized, identity-bound networks This project is released to help researchers, developers, and institutions build provable, reliable, and safe computational systems for the coming decades.
In 5G/6G networks, Device-to-Device (D2D) Salvage Transmission (ST) ensures communication continuity when Unreachable User Equipment (X) loses access to the base station. However, securing authentication between X and Salvage User Equipment (S) while maintaining privacy presents a challenge. Traditional authentication methods depend on key exchanges or centralised servers, heightening vulnerability to impersonation and replay attacks. This paper proposes a Zero-Knowledge Proof (ZKP)-based authentication protocol that enables lightweight, privacy-preserving authentication without disclosing private credentials. The approach utilises modular exponentiation and cryptographic hashing, allowing X to demonstrate its legitimacy without revealing its private key. Simulation results indicate that the proposed method achieves low authentication latency ( 1–10 ms), minimal communication overhead ( 80 bytes per session), and high scalability under heavy authentication loads. The scheme provides resistance against man-in-the-middle (MITM), impersonation, and replay attacks, making it highly secure for real-time applications.
Advanced Authentication Protocols Security
Wireless Communication Security Techniques
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract In today’s world, most financial and personal transactions happen online. This makes data security a big concern. To address risks like data breaches, hacking, and identity theft, our project “Blockchain Secure Transaction” aims to create a dependable and decentralized system for secure digital payments. The system uses blockchain technology to ensure transparency and immutability in each transaction, eliminating the need for a central authority. The process starts with user registration, where details are securely stored along with a picture password for better recognition. During login, users must pass both the picture password and a biometric check. This ensures that only the account. Once verified, the user enters the dashboard, where transactions begin through Zero-Knowledge Proof (ZKP) for privacy-preserving verification. Every transaction is validated with smart contracts. If there’s any mismatch or automatically blocks or freezes the transaction. The backend uses Java, while Firebase stores user data securely, and 2 factor.in enables OTP-based authentication. The frontend interface, designed in React (app.jsx), allows smooth navigation across pages. By combining blockchain, smart contracts, biometric authentication, and ZKP, this project provides a secure and user-friendly platform that prevents unauthorized access and builds user trust in digital payment systems Keywords Blockchain, Secure Transaction, Zero Knowledge Proof (ZKP),Smart Contract, Biometric Authentication, Picture Password, Decentralized System, Data Privacy, Transaction Verification, Firebase Integration, 2 factor.in OTP Authentication.
Mohammad M Maheri, Sunil Cotterill, Alex Davidson, Hamed Haddadi
Machine unlearning aims to remove the influence of specific data points from a trained model to satisfy privacy, copyright, and safety requirements. In real deployments, providers distribute a global model to many edge devices, where each client personalizes the model using private data. When a deletion request is issued, clients may ignore it or falsely claim compliance, and providers cannot check their parameters or data. This makes verification difficult, especially because personalized models must forget the targeted samples while preserving local utility, and verification must remain lightweight on edge devices. We introduce ZK APEX, a zero-shot personalized unlearning method that operates directly on the personalized model without retraining. ZK APEX combines sparse masking on the provider side with a small Group OBS compensation step on the client side, using a blockwise empirical Fisher matrix to create a curvature-aware update designed for low overhead. Paired with Halo2 zero-knowledge proofs, it enables the provider to verify that the correct unlearning transformation was applied without revealing any private data or personalized parameters. On Vision Transformer classification tasks, ZK APEX recovers nearly all personalization accuracy while effectively removing the targeted information. Applied to the OPT125M generative model trained on code data, it recovers around seventy percent of the original accuracy. Proof generation for the ViT case completes in about two hours, more than ten million times faster than retraining-based checks, with less than one gigabyte of memory use and proof sizes around four hundred megabytes. These results show the first practical framework for verifiable personalized unlearning on edge devices.
In this paper, I prove that sublinear regret across the environment Class C requires six functional properties, that these properties are mutually independent, and that they compose into a directed informational chain closing back on itself — a six-link cycle whose final link is grounded in an explicit Doob martingale construction over cycles of play. All six properties are defined functionally — as conditions on the distributions a decision-maker induces over actions and canonical summaries — so the results are invariant under implementation and apply to any decision-making system that can be modelled within the class: a person, an institution, or a machine. Every theorem in this paper, without exception, is checked line by line in the Lean 4 proof assistant against Mathlib: the formalization (~12,700 lines) contains zero `sorry`, zero custom axioms, and zero opaque definitions. Class C is the union of all POMDPs satisfying at least one of six structural properties covering the fundamental qualitative dimensions of adaptive hardness: reward ambiguity (P1), absorbing traps (P2), local optima (P3), deterministic optimality (P4), constrained feasibility (P5), and nonstationarity (P6). * Part I (Necessity). I define six primitives X1–X6 as purely functional properties of decision rules: Objective Tracking, Cross-Context Safety Transfer, Global Attractor Exploration, Policy Simplification, Feasibility Projection, and Feedback Adaptation. For each, I construct an explicit environment in C and prove an unconditional Ω(T) regret lower bound for any decision-maker lacking that primitive.* Part II (Independence). For every ordered pair (i,j) with i≠j, I exhibit an explicit decision rule possessing Xj but lacking Xi that suffers Ω(T) regret on the matching environment. All thirty directed-pair results are shown to follow from one master theorem, verified on a single compound environment with full non-interference analysis.* Part III (Sequential Dependence). Necessity is domain-invariant — a structural failure is a structural failure no matter what "success" means to the decision-maker — which is why Parts I and II hold unconditionally. Sufficiency is not: what counts as success is supplied by the domain, not by the theorem, so a single closed-form sufficiency result covering every domain at once would have to either fix one arbitrary notion of success and stop being general, or say nothing of substance. Part III proves exactly what generalizes. I prove six Information Enhancement Theorems establishing that the six primitives compose into a directed information chain: possessing Xi strictly increases the mutual information available toward any goal variable at Xi+1's task. Each of the six links is established outright — a forward theorem, a reverse theorem, and a non-reversibility result — with the exact point where a domain's own definition of success enters the chain named explicitly, as an Implementation Obligation, rather than assumed away. The closing link, X6→X1, is grounded in an actual Doob martingale construction: given that the cycle-indexed posterior is a martingale, it converges almost surely to the truth across cycles — the precise sense in which the chain accumulates rather than resets. To this paper's knowledge, no prior formalization unifies this many independently-proven-necessary structural properties into a single machine-checked class with proven mutual independence across all of them. All mathematical work is provided in full transparency and independent verification is highly encouraged: the complete Lean formalization, with a passing build and every theorem cross-referenced to its exact identifier, is at github.com/M-Ismail-ZA/IsmailsPrimitives. For any feedback or collaboration, please contact me via the email address listed on the paper. Updated: 3 July 2026 (V6.1).
Zero-knowledge Succinct Non-interactive Argument of Knowledge (zkSNARK) is a powerful cryptographic primitive that enables a prover to convince a verifier that something is true without leaking the private witness. Current zkSNARKs face significant computational costs in generating proofs, which restricts their use in areas like private payments, confidential smart contracts, and anonymous credentials. Private delegation offers a practical solution by outsourcing the heavy computation to powerful external workers without leaking any private information. In this work, we propose HyperSiniel, an efficient private delegation framework for general zkSNARKs that achieves a new feature called guaranteed output delivery (GOD). HyperSiniel is designed to be compatible with any universal zkSNARKs constructed from a polynomial interactive oracle proof (PIOP) and a polynomial commitment scheme (PCS). It enables a computationally limited delegator to outsource proof generation to several workers in a fully non-interactive and privacy-preserving manner. Compared to the most state-of-the-art frameworks (e.g., Siniel [NDSS'25]), HyperSiniel ensures that the delegator always receives a correct proof, regardless of malicious worker behavior. We implement HyperSiniel and compare the performance with Siniel across varying bandwidths and circuit sizes. Under low-bandwidth conditions (10MBps), HyperSiniel incurs only an additional 25% overhead compared with Siniel, while the total running time of HyperSiniel is almost identical to Siniel under high-bandwidth settings (1000MBps). These results show that the strong robustness guarantee of GOD in HyperSiniel comes almost for free, making it a practical and secure solution for real-world zkSNARK delegation.
Open access
Cryptography and Data Security
Blockchain Technology Applications and Security
Physical Unclonable Functions (PUFs) and Hardware Security
While decentralized authentication mechanisms have gained significant attention for enabling user-centric identity management without centralized authorities, the critical counterpart - authenticated key exchange (AKE) in decentralized settings - remains understudied. Although it forms the basis for secure communication in decentralized scenarios, shifting existing AKE protocols to decentralized settings is impractical: the trust assumption is different, and the insufficient support for dynamic identity attributes, etc. To address these challenges, we present a novel decentralized AKE protocol that innovatively integrates attribute authentication with key exchange through multi-party secure computation. Building upon MPCAuth's foundational framework (S&P 23), our protocol goes further to provide key exchange based on authentication of real-world attributes such as a digital passport and email address, etc. Our protocol establishes a new paradigm for decentralized AKE without complex credential operations and heavy zero-knowledge proof. The core of our protocol is a distributed way to securely reconstruct the attributes and establish a session key. We further evaluate its performance across multiple servers. Experimental results on servers under 5 demonstrate that it can finish the full AKE procedure in an acceptable time, enabling efficient and scalable multi-party key AKE in distributed environments.
Anurag Anand Duvey, Chandrashekhar Goswami, Amit Kumar Goel
Federated Learning (FL) gives opportunity to decentralized model training without the raw data's revealing. But in actual real-world implementation faces certain number of challenges. These include trust in client updates, verifiable end-to-end privacy promises, equitable contributor compensation, and accountable aggregation. This paper provides a design-first architecture that addresses these issues by integrating concise zero-knowledge proofs (zk-SNARKs) for trustless verification with smart-contract arrangements. This approach is for verification, secure aggregate pooling, and reward settlement. Our design is consisting a structure of five-layer stack, named as Client, Proof, Blockchain, Incentive, and Governance. It highlights end-to-end workflows for the generation of proof for updates, verifying them on chain, anchor-off chain aggregation sequence anchoring, and allocate contribution-matching token payouts. We specify clearly smart-contract interfaces called as aggregation, registry, incentivization, zk-circuit objectives, and several scaling controls like proofs/aggregated in batch or Layer-2/rollup rollouts and the anchor-Merkel. The design also implements Shapley-like contribution measures and ERC-compact reward settlement. It incorporates Sybil-resistance and vesting primitives to lean against game. We define a crisp threat model, discuss security and privacy trade-offs, and suggest evaluation using healthcare and IoT benchmarks. These assess the learning utility, resilience to poisoning, fairness of the payouts, and system costs in terms of gas fee. Moreover, by training and incentivization by smart contracts at design level, we create a foundation for future prototyping and rigorous empiric testing. This sets a realistic path from the design framework to effective workable, end-to-end Privacy-preserving Federated Learning deployment.
Federated Learning (FL) enables multiple participants to collaboratively train a shared model without sharing their private data. However, FL remains vulnerable to malicious clients submitting incorrect updates to disrupt training. To address this, we formalize each client’s local training step as a Nondeterministic Polynomial-time (NP) statement, verifiable via zero-knowledge proofs (ZKPs) at every round. We propose Jolt-FL, the first general-purpose verifiable FL framework that immediately detects and excludes malicious clients upon their first dishonest action – without relying on heuristics, statistical assumptions, or multi-round analysis. Built on Jolt’s zkVM, a state-of-the-art zero-knowledge virtual machine (zkVM) developed by a16zcrypto, Jolt-FL guarantees training integrity and data privacy without trusted hardware or third-party intermediaries. By witnessing every computation step, it defends against a wide range of attack vectors, securely filtering dishonest updates even if up to 50% of clients are malicious, while preserving convergence and final model performance. To demonstrate feasibility, we implement a prototype featuring a complete end-to-end Convolutional Neural Network (CNN) for image classification using the MNIST dataset. To our knowledge, this is the first fully verifiable end-to-end CNN training under ZKPs without any custom circuit design. Our solution achieves competitive proof generation times, compact proof sizes, and low verification costs–all while preserving model accuracy on par with standard FL.
Johnny Y. Solano Marinho, Eryk Schiller, Arthur Debauge, Noria Foukia
This paper presents a framework that integrates the Coalition for Content Provenance and Authenticity (C2PA) standard with Zero-Knowledge Proofs (ZKPs), specifically the Zero-Knowledge Succinct Non-Interactive Arguments of Knowledge (zk-SNARKs), to enable verifiable yet privacy-preserving authentication of digital images. Using the ZoKrates toolkit, the system derives a non-revealing fingerprint from the image, generates a succinct proof of integrity, and embeds this proof into C2PA-compliant metadata without exposing the underlying content. The proof can be verified locally or on the Ethereum blockchain using a Groth16 smart contract verifier, providing decentralized and auditably transparent validation. This capability allows journalists, victims, and legal professionals to attest to the existence and integrity of sensitive evidence while deferring its disclosure. Experimental results show that proof verification is highly efficient, requiring approximately 0.01 s, and that the entire workflow is reproducible within containerized environments. The proposed integration of zk-SNARKs with C2PA establishes a practical foundation for secure digital provenance, privacy-preserving evidence management, and strengthened trust in digital media ecosystems.
Abstract The accelerating dominance of non-human agents in digital infrastructures has created an existential imbalance between human intentionality and synthetic computation. All identity-centric and post-hoc verification paradigms have failed against advanced automation. This work introduces Proof of Being (PoB) — an ontological cryptographic primitive that binds digital agency to continuous, embodied human presence without revealing identity. Using Human Intention Semantic Proof Units (HISPU) — the fusion of physiological dynamics, semantic activity structure, and changing environmental context — we generate zero-knowledge proofs of authentic human engagement. From this substrate emerges the Vital Presence Token (VPT), a new energy-like digital asset that supplies “existential energy” exclusively to human-authorized agents. Ontological Digital Agents Management (ODAM) implements a biological-immune-system analogue: agents lacking fresh VPT undergo ontological death and cannot claim computational resources. The framework establishes verifiable human presence as the constitutional substrate for post-AGI digital civilization and defines the economic foundations of Web4. Keywords: proof of being, ontological cryptography, human presence verification, digital immune system, vital presence token, Web4, post-AGI governance, zero-knowledge biometrics, existential energy, digital agents management, human-in-the-loop, semantic intentionality, biological computing, HLA/MHC analogy, human sovereignty, scarcity, decentralized identity, ODAM, HISPU.
National identity systems require efficient, equitable decision-making that safeguards personal data. This article proposes a Self-Sovereign Identity (SSI) architecture, supported by a Verify-Without-Reveal (VWR) framework, designed for national-scale implementation. SSI places credentials in a citizen wallet and enables selective disclosure and zero-knowledge proofs, so services can verify attributes without seeing underlying records. VWR adds the policy and accountability spine: yes/no attribute APIs for holder-absent cases, purpose-bound and zero-trust enforcement on every call, and an immutable audit layer on a permissioned ledger. The study synthesises current standards and leading implementations in Europe and worldwide and formulates a deployable blueprint with clear roles, consent and lawful-override flows, per-agency pseudonyms, and regulator and citizen visibility. The study outlines reference APIs, user experiences for wallets and verifiers, and performance metrics suited for national workloads. Privacy-preserving AI strengthens biometric liveness, fraud detection, and anomaly response without centralising sensitive data. The framework aligns with GDPR data minimisation and purpose limitation, supports the European Digital Identity Wallet, and meets high-risk AI governance requirements. Results show how SSI proofs and VWR controls reduce unconsented disclosure and cross-agency browsing, while keeping latency low and interoperability high. The contribution is both conceptual and operational: a phased migration path that turns verify-without-reveal into the default mode for government and regulated services, improving security, inclusion, and public trust.
The digital transformation of education necessitates secure, private, and learner-centric methods for verifying academic credentials. Conventional verification processes expose sensitive personally identifiable information, creating privacy risks that conflict with data protection regulations like GDPR. Existing blockchain solutions for educational credential verification face persistent challenges including prohibitive transaction costs, privacy vulnerabilities, and inflexible verification models. This paper presents VeriZKP, a proof-of-concept architecture demonstrating gas-free credential verification on Ethereum using zero-knowledge proofs. The core innovation lies in separating on-chain trust anchoring from off-chain cryptographic computation, enabling a novel cost-elimination mechanism. The system leverages Ethereum’s view functions through pre-compiled verifier contracts to achieve zero gas consumption for verification operations while preserving privacy through selective disclosure mechanisms. Our prototype, evaluated on Ethereum Sepolia testnet, validates the fundamental feasibility of this approach. Results demonstrate complete elimination of verification costs, practical client-side proof generation times of 1.02-1.63 seconds on standard hardware, and support for multi-attribute credential verification. The architecture proves both economically viable and performant for blockchain-based identity systems.