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.
Accounting is undergoing a radical transformation due to the integration of traditional information systems with blockchain technology and artificial intelligence. Openness, automation, and smart decision-making will all become a reality via this connection. However, traditional SAIS are typically centralized and do not inherently include blockchain or AI. In this study, Smart Accounting Information System (SAIS) technologies are redefined through the integration of these technologies to enhance transparency, automation, and real-time assurance. Blockchain technology's immutability, traceability, and AI's ability to recognize abnormalities and predict provide a more intelligent and secure auditing process. Conventional accounting methods have several issues, including delayed audits, lack of transparency, fraud, and human mistakes. Existing systems fail to provide intelligent anomaly detection and real-time transaction traceability. Financial reporting and audits need immutable records and proactive analytics. There is an urgent need for a single framework to ensure this requirement and its quick implementation. This study proposes the collaborative blockchain-AI audit trails method (CBAATM) for Smart Accounting Information Systems. This is done due to the difficulties mentioned. AI-powered modules utilize fuzzy inference to dynamically analyze audit risks and Random Forest classifiers to detect real-time fraud. This research project utilizes zero-knowledge proofs and homomorphic encryption to simultaneously handle data aggregation, privacy, and independent audits. Using middleware application programming interfaces makes integration with ERP and AIS systems easy. Throughout the testing process, the model outperforms conventional audits. The methodology, according to statistical research, ensures the detection accuracy ratio of 95%, integrity of the blockchain 99.2% of the time, identifies abnormalities 94.1% of the time, satisfies compliance standards 95.4% of the time, and reduces audit latency by 41.5% compared to other existing models.
This is a derivative of the German version that you can find here. Many modifications and improvements have been made in this version. The Gaia Economy – The VisionThis new economic and monetary system is a project for structural balance. It addresses the feelings and incentives of the wealthy, the middle class, and the poor alike. Critically, this new economic and monetary system makes it significantly easier to establish genuine social-democratic systems. Instead of allowing inequality to develop unchecked – which must then be corrected by taxing the rich – the Gaia Economy preventatively stops the accumulation and hoarding of wealth from the start. A central mechanism is demurrage (a circulation-maintenance fee): money does not need to be “recaptured” through taxes; instead, a continuous stream of funds is created by the natural decay of idle balances. Technically, this means: Treasury Accrual: Idle balances pay a small fee (e.g., 0.5% per month) into a transparent Treasury. Operations & Impact: This Treasury funds system operations (security, audits) and the Impact Layer. Separation: The Impact Layer decides allocations based on transparent, verifiable criteria, but it never gates or controls the Payment Layer. Status & ImplementationThis manuscript represents the first half of the complete work; further chapters detailing advanced implementations and global scaling are forthcoming. However, we are not waiting for the text to be finished to act. The Payment Layer and Impact Layer have already been programmed. They are fully functional and ready to use as an application. This app will be released officially alongside the implementation of the first pilot project. The Gaia Economy is conceived as a learning system – errors are data that can be changed through a rigorous governance process. We invite you to build, test, and improve with us. Collaboration requests, constructive criticism, and questions are highly welcome. Contact: info@dzydent.com Abstract: The Gaia Economy (U.S. Edition) The DiagnosisThe current monetary system contains a structural flaw: positive interest and compound interest automatically shift wealth upward, generating permanent pressure for growth and rationalization. This "invisible vacuum cleaner" siphons purchasing power from the real economy into financial asset hoards. The Solution: Two Separated Modules The Gaia Economy introduces a new economic infrastructure consisting of two deliberately separated layers: Payment Layer (Gaia Coin): A neutral, non-speculative payment rail. It anchors a light circulation pressure (demurrage) in code. This ensures money keeps flowing, making hoarding unattractive. It serves as a medium of exchange, not a wealth storage vehicle. It is non-custodial and privacy-preserving (no on-chain PII), utilizing zero-knowledge proofs (ZKPs) to validate transactions without disclosing personal details. Impact Layer (Voluntary Incentives): An optional layer that rewards verifiable contributions to the common good (e.g., ecological repair, care work, education). It operates on a cash-basis: rewards (Vouchers) are paid out of realized Treasury inflows, ensuring the system never creates debt or inflation. It evaluates entities, not individuals, preventing "social credit" surveillance. Governance & SafeguardsTo prevent capture, the Gaia Economy utilizes common-good councils and a multi-quorum governance system. Changes to core parameters require a supermajority and a mandatory timelock (delay), ensuring no rule changes happen overnight. Implementation StrategyIntroduction proceeds via closed-loop pilots (municipalities, universities, merchant associations) that run in parallel with the U.S. Dollar. The Gaia Economy is positioned as complementary infrastructure – compatible across political camps – secured through clear legal frameworks (e.g., 501(c)(3) stewardship, licensed partners for fiat ramps). Executive Summary (For Decision-Makers) Starting Point & GoalThe Gaia Economy responds to structural mis-incentives in the existing monetary system (hoarding, wealth concentration, growth pressure) with a practical, legally grounded alternative that runs voluntarily in parallel to the USD. Core Solution Gaia Coin (Payment Layer): A digital cash replacement with embedded demurrage to stimulate local circulation. Architecture: Energy-efficient consensus, pseudonymous wallets, open-source code. Neutrality: Payments are never gated by behavior or AI. Impact Layer (Incentives): A voluntary layer that rewards verifiable outcomes. Mechanism: Impact Vouchers are minted for verified actions and redeemed for Gaia Coin. Pacing: Payouts are strictly paced by the Budget_k (realized treasury inflow) to ensure solvency. Verification: Relies on off-chain evidence and Human-in-the-Loop review; AI is assistive only. Governance & Compliance (U.S. Context) Immutable Core: The separation of Payment/Impact and the prohibition of positive interest are unchangeable. Parameter Registry: Adjustable parameters (e.g., demurrage rate) require Supermajority + Timelock. Compliance: Pilots start as non-custodial closed loops. Any custody or fiat interaction is handled exclusively by licensed partners (banks/MTLs), ensuring compliance with U.S. regulations without burdening the protocol. Introduction & Scaling Phase 1 (Pilot): Private, closed-loop implementation with anchor merchants and a local nonprofit. Phase 2 (Regional): Integration with municipal services and licensed on/off-ramps. Phase 3 (Network): Inter-regional connection. Benefits Short term: Faster local circulation (Velocity), reduced merchant transaction costs, transparent funding for local projects. Mid term: Measurable strengthening of care, education, and environmental protection through the Impact Layer. Long term: A socially stable, ecologically compatible economy that relies on incentives rather than coercion. Immediate Next Steps The software and the blockchain currency are ready. The path forward is execution: Sign non-controlling MOUs with pilot partners (City/University). Define the Impact Catalog v1 (verifiable metrics for local needs). Deploy the Protocol v1.0 App (Wallet + POS + Treasury Dashboard). Establish Governance (GIP process, Council selection). Deploy Monitoring (Public Dashboards for Treasury and Impact KPIs). Keywords: Gaia Economy, Demurrage, Dual-Module Economic Architecture, Cash-Basis Budgeting, Impact Vouchers, Non-Custodial, DAO Governance, Social Democracy, Justice, Fair, Anti-Hoarding, Common Good, Sustainable Development.Work to be done next:Part X – Technical Blueprint & Pilot-to-Scale Roadmap (expanded, detailed, integration-ready) Part XI – The Mathematics of GAIA (Balance Equations) (10-point micro-structure per subsection) Part XII – Environment, Animals, Public Health (Special Topics) (10-point micro-structure per subsection) Part XIII – Practice: U.S. Case Studies (10-point micro-structure per subsection)
As vehicular ad hoc networks (VANETs) increase in size and complexity, ensuring secure, flexible, and privacy-preserving vehicle-to-infrastructure (V2I) authentication remains a major challenge. Existing protocols often focus solely on identity verification, overlooking the need for access control based on vehicle attributes. Furthermore, vehicles must obtain authentication credentials from various trusted entities, including automakers, regulators, and government agencies. However, the absence of a unified credential issuance mechanism introduces fragmentation and inconsistencies during the registration process. To address these issues, we propose a V2I authentication protocol, called PriV2I, that integrates distributed credential issuance, attribute-based access control, and strong anonymity guarantees. During vehicle registration, our approach uses Shamir’s Secret Sharing with a threshold t of n across multiple certification authorities (CAs) to consolidate credentials. A vehicle credential can only be issued by a predefined threshold number of CAs, enhancing security and flexibility. Within the authentication protocol, Pointcheval-Sanders (PS) signatures enable fine-grained access control based on vehicle attributes such as type and role. Meanwhile, noninteractive zero-knowledge proofs protect identity privacy by allowing vehicles to prove credential possession and policy compliance without revealing sensitive information. The proposed scheme also supports batch authentication at Roadside Units (RSUs) to efficiently handle high-density environments and includes a comprehensive revocation mechanism to trace and revoke malicious vehicles promptly and securely. In our implementation, the computation cost during the authentication phase is 75.58 ms. The communication overhead per authentication exchange is 992 bytes across two messages. Overall, the protocol provides a secure, scalable, and privacy-preserving solution tailored to modern VANET environments.
Vikash Singh, Little, Barrett, Phil Hayes, Fang, Max · 7 authors
Verifying the private liquidity state of Lightning Network (LN) channels is desirable for auditors, service providers, and network participants who need assurance of financial capacity. Current methods often lack robustness against a malicious or compromised node operator. This paper introduces a methodology for the verification of LN channel balances. The core contribution is a framework that combines Trusted Execution Environments (TEEs) with Zero-Knowledge Transport Layer Security (zkTLS) to provide strong, hardware-backed guarantees. In our proposed method, the node's balance-reporting software runs within a TEE, which generates a remote attestation quote proving the software's integrity. This attestation is then served via an Application Programming Interface (API), and zkTLS is used to prove the authenticity of its delivery. We also analyze an alternative variant where the TEE signs the report directly without zkTLS, discussing the trade-offs between transport-layer verification and direct enclave signing. We further refine this by distinguishing between "Hot Proofs" (verifiable claims via TEEs) and "Cold Proofs" (on-chain settlement), and discuss critical security considerations including hardware vulnerabilities, privacy leakage to third-party APIs, and the performance overhead of enclaved operations.
AI development requires reliable datasets, yet today’s data supply chains face challenges in traceability, authenticity and ethical compliance. This study introduces a blockchain-assisted data integrity framework that ensures transparent and verifiable provenance for AI model training. The proposed system uses smart contracts to record data lineage, ownership, preprocessing transformations and annotation events. IPFS-based off-chain storage reduces blockchain load while ensuring immutability. A verification engine allows auditors to evaluate dataset compliance with ethical and regulatory standards, including bias mitigation and consent validation. Experiments utilized three real AI workflows: medical imaging, sentiment analysis and environmental sensor classification. Findings show a 92 percent reduction in provenance disputes and an improvement in audit efficiency by 41 percent. The system also provides tamper-resistant documentation supporting responsible AI governance. Latency tests show minimal performance impact due to parallelized validation nodes. This research demonstrates that blockchain can provide a robust backbone for ethical AI ecosystems, where transparency and trust are critical. Future work will explore confidentiality enhancements using zero-knowledge proofs.
Raghavan Sheeja, Sherwin Richard R., Shreenidhi Kovai Sivabalan, Srinivas Madhavan
<p>The generational improvement has significantly converted several industries, and the area of intellectual property rights (IPR) isn’t any exception. IPRs, being as important as they are, need to be securely managed in some way. Blockchain, with its decentralized and immutable nature, gives a promising answer for enhancing the management of intellectual property (IP). This paper explores the strategic integration of blockchain generation for the control of IPR. The proposed system consists of a complete system, from registration and validation to predictive evaluation and royalty distribution, all facilitated through clever contracts. The use of zero-knowledge proofs guarantees the safety and confidentiality of sensitive information. The paper discusses the advantages and future implications of implementing this type of device.</p>
The complexity class Quantum Statistical Zero-Knowledge ($\mathsf{QSZK}$), introduced by Watrous (FOCS 2002) and later refined in Watrous (SICOMP, 2009), has the best known upper bound $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$, which was simplified following the inclusion $\mathsf{QIP(2)} \subseteq \mathsf{PSPACE}$ established in Jain, Upadhyay, and Watrous (FOCS 2009). Here, $\mathsf{QIP(2)}$ denotes the class of promise problems that admit two-message quantum interactive proof systems in which the honest prover is typically computationally unbounded, and $\text{co-}\mathsf{QIP(2)}$ denotes the complement of $\mathsf{QIP(2)}$. We slightly improve this upper bound to $\mathsf{QIP(2)} \cap \text{co-}\mathsf{QIP(2)}$ with a quantum linear-space honest prover. Specifically, the honest prover uses space linear in the size of the transcript of the original $\mathsf{QSZK}$ proof system. A similar improvement also applies to the upper bound for the non-interactive variant $\mathsf{NIQSZK}$. Our main techniques are algorithmic versions of the Holevo-Helstrom measurement and the Uhlmann transform, both implementable in quantum linear space, implying polynomial-time complexity in the state dimension, using the recent space-efficient quantum singular value transformation of Le Gall, Liu, and Wang (CC, to appear).
In the decentralized Internet environment, growing awareness of user data sovereignty has raised higher requirements for privacy protection in blockchain scenarios. To enhance the security and controllability of data authorization, this study develops a model integrating zero-knowledge proof (ZKP), field disclosure control, and multi-party joint verification. The ZKP ensures verifiable privacy, field disclosure control minimizes data exposure, and multi-party verification strengthens consistency and tamper resistance. Through this collaborative integration, the model forms a unified framework for secure and transparent data authorization. Experimental results on two blockchain datasets show that the model outperforms comparison approaches in authorization accuracy, field matching consistency, and verification efficiency, achieving a minimum verification loss of 0.248 and a true positive rate of 96.8%. Under simulation conditions, it maintains stable performance across different complexity levels, with authorization accuracy of 95.1% and field validation consistency of 96.5%. Compared with traditional single-mechanism methods, the model delivers comprehensive improvements in privacy strength, verification transparency, and collaborative trust, demonstrating strong potential for application in high-sensitivity blockchain privacy protection scenarios, particularly in privacy-critical domains such as healthcare record management, financial data exchange, and supply chain traceability.
Vinoth Punniyamoorthy, Ashok Gadi Parthi, Mayilsamy Palanigounder, Ravi Kiran Kodali · 6 authors
Distributed machine learning systems require strong privacy guarantees, verifiable compliance, and scalable deployment across heterogeneous and multi-cloud environments. This work introduces a cloud-native privacy-preserving architecture that integrates federated learning, differential privacy, zero-knowledge compliance proofs, and adaptive governance powered by reinforcement learning. The framework supports secure model training and inference without centralizing sensitive data, while enabling cryptographically verifiable policy enforcement across institutions and cloud platforms. A full prototype deployed across hybrid Kubernetes clusters demonstrates reduced membership-inference risk, consistent enforcement of formal privacy budgets, and stable model performance under differential privacy. Experimental evaluation across multi-institution workloads shows that the architecture maintains utility with minimal overhead while providing continuous, risk-aware governance. The proposed framework establishes a practical foundation for deploying trustworthy and compliant distributed machine learning systems at scale.
The article presents an analysis of the robustness of an authentication scheme based on zero watermarking. The study examines a two-factor authentication scheme that uses "knowledge of something" (a password) and "possession of something" (a digital RGB image) as its factors. The zero watermarking algorithm chosen is based on DWT and K-means transformations, with additional use of the Swish function. The analysis is conducted by considering the theoretical complexity of the algorithm assuming the adversary knows its parameters, such as the password, the hash of the password, the image, the reference watermark, the transformation result, and other parameters. Previous studies have shown a high theoretical robustness of the scheme, which relies on the complexity of the password and the dimensionality of the image. For large image sizes (512×512 pixels and above), a relatively high level of cryptographic resistance is achieved. However, this robustness is not formally proven, and the actual strength may be significantly lower due to the specifics of the images and transformations, which can introduce additional vulnerabilities. The algorithm is subject to a relatively high rate of collision, associated with digital image transformations and matrix multiplications, which weakens its resistance. Authentication schemes and zero watermarking algorithms require further research, formal proof of cryptographic properties, and methods for integration into access control systems, as they can provide a high level of authentication robustness in systems with high noise levels. Additionally, the convenience and low cost of such schemes give them an advantage over other authentication methods. The study provides recommendations for improving the potential characteristics of the algorithm.
Open access
Advanced Steganography and Watermarking Techniques
The rapid proliferation of Internet of Things (IoT) devices across various industries, including healthcare, smart cities, and industrial automation, has introduced significant security, authenticity, and traceability challenges within increasingly complex supply chains. Although existing approaches have utilised blockchain-based digital identity solutions to address some of these concerns, persistent issues of counterfeit products and inadequate lifecycle transparency highlight the need for more robust, hardware-anchored identification mechanisms. Our work presents a novel architecture that integrates Physically Unclonable Functions (PUFs) and blockchain-based Soulbound Tokens (SBTs) to establish secure and verifiable digital identities directly tied to the physical hardware of IoT devices. By employing cryptographic tools such as fuzzy extractors, Merkle trees, and zero-knowledge proofs, the proposed architecture ensures accurate lifecycle tracking through key operational stages, including manufacturing, procurement, provisioning, maintenance, and eventual disposal or recycling. Performance evaluations conducted on the Ethereum Sepolia testnet demonstrate reasonable computational overhead in terms of gas usage and transaction confirmation times. The findings reveal that this approach aligns with NIST Special Publication 800-161 guidelines, as well as emerging regulatory standards, notably the European Union’s Digital Product Passport initiative, and has significant implications for enhancing transparency, sustainability, and security across global IoT supply chains.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract Zero knowledge succinct non-interactive arguments of knowledge protocol (zk-SNARK) is an application oriented variant of zero knowledge proof, which enables a prover to convince a verifier that a statement is true, without revealing any other information beyond the correctness of the statement itself. Due to its powerful capabilities and high efficiency, it has been widely deployed in various blockchain based applications to provide privacy and scalability. While these applications place high demands on small proof size, fast verification and decentralization, currently available zk-SNARK with the shortest proof size and the fastest verification speed is in the common reference string (CRS) model, that is they require the trusted setup. After the pioneering results proposed by Bellare et al. in ASIACRYPT 2016, there have been lots of efforts to construct zk-SNARKs that satisfy subversion zero knowledge (S-ZK) and standard soundness from the zk-SNARK in the CRS model. These constructions could be regarded secure in the bare public key (BPK) model because that the equivalence between S-ZK in the CRS model, and uniform non-black-box zero knowledge in the BPK model has been proved by Abdolmaleki et al. in PKC 2020. Thus, compared to the CRS model, the BPK model better characterizes decentralized blockchain based application such as cryptocurrencies and anonymous credentials. In this study, by leveraging the power of random oracle (RO) model, we proposed the first publicly verifiable non-uniform ZK zk-SNARK scheme in the BPK model maintaining comparable efficiency with its conventional counterpart, which can also be compatible with the well-known transformation proposed by Bitansky et al. in TCC 2013 to obtain an efficient designated-verifier zk-SNARK. We achieve this goal by only adding a constant number of elements into the CRS, and using an unconventional but natural method to transform Groth’s zk-SNARK in EUROCRYPT 2016. In addition, we propose a new speed-up technique that provides a trade-off. Specifically, if a logarithmic number of elements are added into the CRS, according to different circuits, the CRS verification time in our construction could be approximately 9–23% shorter than that in the conventional counterpart.
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.
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.
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.
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.
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).