Every standard signature scheme enforces one property: only the key holdercan sign. What the key holder signs is unconstrained. Policy enforcement-- spending limits, rate limits, access control -- lives in smartcontracts, middleware, or governance: layers that can be upgraded,bypassed, or exploited. We call this the software-layer assumption:compliance holds only if the enforcing code is correct and unmodified. We eliminate this assumption. We introduce behavior-bound signatures(BBS), in which a policy constraint delta(x) < epsilon is committed atkey generation and enforced inside the signature's zero-knowledge proof.If the action violates the policy, the ZK constraint system isunsatisfiable -- no witness, no proof, no signature. This is not asoftware check. It is a mathematical impossibility. No software canoverride. Unlike policy-based signatures (where an authority imposes policy onsigners), BBS is self-committed: the signer binds their own futurebehavior at key generation, and even the signer cannot later violate orrevoke this commitment. We formalize this as policy-soundness (PS-CMA), a security modelstrictly stronger than EUF-CMA, and prove it under standard assumptions(Pedersen binding, Poseidon CR, ZK knowledge soundness). From thissingle primitive, five independent consequences follow -- not as separatedesigns, but as necessary implications of one cryptographic root: (A) Compliance safety under f <= n-1 Byzantine faults, decoupled from honest-quorum assumptions.(B) O(1) verification and audit via a single ZK check and Pedersen homomorphic aggregation.(C) Elimination of the virtual-machine execution layer for policy-constrained transactions.(D) A gasless ledger: branch C removes metering, while ZK-encoded rate limits make spam mathematically nonexistent.(E) The first cryptographic guarantee that a compromised autonomous AI agent cannot exceed its authorized behavioral envelope.
Open access
3 source records
Cryptography and Data Security
Advanced Authentication Protocols Security
Physical Unclonable Functions (PUFs) and Hardware Security
Sybil resistance is a key requirement of decentralized consensus protocols. It is achieved by introducing a scarce resource (such as computational power, monetary stake, disk space, etc.), which prevents participants from costlessly creating multiple fake identities and hijacking the protocol. Quantum states are generically uncloneable, which suggests that they may serve naturally as an unconditionally scarce resource. In particular, uncloneability underlies quantum position-based cryptography, which is unachievable classically. We design a consensus protocol that combines classical hybrid consensus protocols with quantum position verification as the Sybil resistance mechanism, providing security in the standard model, and achieving improved energy efficiency compared to hybrid protocols based on Proof-of-Work. Our protocol inherits the benefits of other hybrid protocols, namely the faster confirmation times compared to pure Proof-of-Work protocols, and resilience against the compounding wealth issue that plagues protocols based on Proof-of-Stake Sybil resistance. We additionally propose a spam prevention mechanism for our protocol in the Random Oracle model.
The proliferation of cloud infrastructures has intensified concerns regarding data security, integrity, identity and access management, and user privacy. Despite recent advances, existing solutions often lack comprehensive integration of privacy-preserving mechanisms, dynamic trust management, and cross-provider interoperability. This study proposes an AI-enabled, zero-trust, blockchain-fused identity management framework for secure, privacy-preserving multi-cloud environments. The framework integrates homomorphic encryption with differential privacy for aggregate-level protection and secure multi-party computation for collaborative data processing. The proposed system was validated in a simulated multi-cloud environment using CloudSim, Ethereum blockchain, and AWS EC2. Experimental results indicate homomorphic encryption latency of approximately 450ms per operation and statistically significant security improvements (t(128) = 12.47, p < 0.001), privacy (t(95) = 8.93, p < 0.001), and throughput (t(156) = 15.21, p < 0.001). The framework achieved differential privacy with Īµ = 0.1 while retaining 99.2% data utility, and demonstrated a 34% improvement in processing speed over conventional differential privacy approaches. In addition, the implementation was observed to be 2.3Ć faster than BGV-based configurations, with 45% lower memory consumption than CKKS and a 67% reduction in ciphertext size relative to baseline implementations. From an operational perspective, the framework shows a 23% reduction in security management costs, a 31% improvement in resource utilization efficiency, and an 18% decrease in compliance audit expenses. The model further indicates a 27% reduction in total cost of ownership (TCO) compared with multi-vendor security solutions, a projected return on investment (ROI) within 14 months, and an 89% reduction in security incident response costs under the evaluated conditions.
Longbo Han, Xiaodong Li, Lin You, Gengran Hu Ā· 8 authors
Vehicular ad-hoc networks (VANETs) require authentication mechanisms that simultaneously deliver privacy, accountability, and timely cross-domain synchronization. The existing schemes struggle to balance unlinkable anonymity with effective tracing. They are also vulnerable to future quantum adversaries and rely on slow and costly revocation workflows. We present ZebraCPA, a decentralized conditional privacy-preserving authentication (CPPA) framework that combines lattice-based traceable ring signatures (TRS) with zero-knowledge (ZK) proofs and a consortium blockchain. Our TRS design removes linkability tags and embeds a tracing trapdoor only recoverable by the authorized auditors. It naturally extends to threshold tracing for multi-auditor settings. To avoid the plain-text key escrow, ZebraCPA leverages the additively homomorphic property of the commitments to support the ciphertext-only key updates by the vehicles, preventing the catastrophic key leakage at authorities. A hierarchical blockchain layer provides fast, consistent synchronization of active-key status across regions. The experiments show 1.7Ćā7.0Ć speedups over state-of-the-art baselines in signing/verification while retaining an anonymity-set size of N=10. The network-level simulations further indicate that ZebraCPA reduces an average packet delay by 30.7% - 61.6% compared with the baselines under moderate traffic densities. Moreover, the security of ZebraCPA is validated through our informal analysis under the Dolev-Yao model. Overall, ZebraCPA achieves post-quantum security, strong anonymity with conditional traceability, and practical deployment efficiency for VANETs, outperforming the existing solutions in terms of both latency and robustness.
Anthony Uchenna Eneh, Love Allen Chijioke Ahakonye, Jae Min Lee, Dong-Seong Kim
Modern auto-insurance workflows require sharing heterogeneous digital evidence across multiple organizations. Yet, current cloud-based role-based access control mechanisms remain coarse-grained and poorly suited for expressing time, purpose, and case-specific constraints. This study presents ClaimGuard, which addresses these limitations by placing a blockchainbacked attribute-based access control gateway in front of existing evidence stores, enforcing fine-grained on-chain policies, and issuing short-lived capability tokens for authorized access. Implemented as a REST gateway with PureChain smart contracts, ClaimGuard is evaluated using realistic workloads involving up to 200 subjects and 1000 evidence resources. Experiments on a local Ethereum network shows sub$\sim 70 ~\text{ms}$tail latency, throughput exceeding$\sim 1000$requests/s, rapid policy updates, and zero false accepts, demonstrating the practicality of decentralized, auditable access control for privacy-preserving claims evidence sharing.
Jay BojiÄ Burgos, Urban Sedlar, Matevž PustiÅ”ek
Abstract Integrating third-party Machine Learning (ML) models into industrial Operational Technology (OT) creates a procurement deadlock: operators cannot verify vendor performance claims without exposing sensitive operational data, while vendors refuse to reveal proprietary model weights before purchase, rendering traditional safeguards such as Non-Disclosure Agreements technically unenforceable. This paper introduces a framework combining Zero Knowledge Proofs (ZKPs) with smart contracts to enable trust-minimized, privacy-preserving competitive model procurement in Industrial Cyber-Physical Systems (ICPS). Our framework allows vendors to cryptographically prove that their model outperforms a legacy baseline without disclosing proprietary weights, a process we term cryptographic performance attestation . The on-chain workflow combines escrow-backed procurement, automated proof verification, and best-vendor selection with arbiter-based dispute resolution. We analyze three distinct ZKP workflow variations for industrial suitability and evaluate their performance on consumer-grade hardware, achieving proving times of approximately three seconds and sub-dollar on-chain verification costs under Layer-2 fee assumptions for the recommended single-proof variation. Results demonstrate the feasibility of pre-deployment model verification while identifying computational trade-offs of recursive proof aggregation. The entire verification phase operates offline with no impact on real-time OT control paths, bridging the IT/OT pre-transaction trust gap while deferring artifact deployment to existing OT tooling.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption combined with parallel processing architecture to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 500,000 transactions per second with parallel processing and 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 1000Ć over existing privacy-focused systems while maintaining equivalent security properties through formally proven cryptographic guarantees.
The convergence of social networking and electronic commerce has given rise to the social e-commerce paradigm, where content creators serve as the primary drivers of consumer engagement and purchase decisions. However, this ecosystem faces a critical tension between the need for high-precision ad targeting to sustain monetization and the increasingly stringent requirements for user privacy preservation. Traditional centralized recommendation systems require the aggregation of massive user behavioral datasets, creating significant risks of data leakage and violating emerging regulatory frameworks. To address this challenge, we propose a novel framework titled Fed-ZKC (Federated Zero-Knowledge Creator). This architecture synergizes Federated Learning (FL) with Zero-Knowledge Proofs (ZKP) to enable privacy-preserving ad targeting while ensuring verifiable monetization attribution for creators. In our system, user preference models are trained locally on edge devices to prevent raw data transmission, while a cryptographic verification layer ensures that ad interactions are genuine without revealing user identities to the platform or the creators. Extensive experiments conducted on large-scale real-world datasets demonstrate that Fed-ZKC achieves recommendation accuracy comparable to centralized baselines while reducing privacy leakage risks by orders of magnitude. Furthermore, the implementation of succinct non-interactive arguments of knowledge (zk-SNARKs) introduces minimal computational overhead, making the protocol feasible for deployment on modern mobile processors.
We propose Trustless Agent Swarms, a framework enabling privacy-preserving coordination among autonomous AI agents on EVM-compatible blockchains. Our system integrates four cryptographic primitives: (1) Groth16 zero-knowledge proofs for proving reputation thresholds without revealing scores; (2) EIP-5564 stealth addresses for unlinkable fund transfers; (3) ERC-4337 account abstraction for gasless autonomous execution; and (4) Semaphore for anonymous group signaling. We implement a 586-constraint reputation proof circuit and deploy five smart contracts on Base Sepolia. Proof generation: 580ms. On-chain verification: 407,576 gas.
Abstract As artificial intelligence (AI) systems become increasingly integrated into critical applications, ensuring trust in their outputs has emerged as a central challenge. Verifiable machine learning (ML) is one approach to addressing this challenge, providing guarantees that results are both correct and reproducible. Existing paradigms, however, provide only partial solutions: zero-knowledge ML (ZKML) achieves strong cryptographic assurances but suffers from limited scalability and high resource costs, while optimistic ML (OPML) supports a wider range of models but relies on economic incentives and long dispute periods. In this work, we propose zk-OPML, a novel hybrid framework that integrates optimistic verification with zero-knowledge proofs (ZKPs). The approach decomposes ML inference into operator-level computations, selectively generating ZKPs for isolated ONNX operators, while retaining the scalability of the optimistic paradigm. We present a prototype implementation and evaluate its performance by benchmarking it against ZKML and OPML. Our results show that zk-OPML achieves faster verification for more complex inference tasks and scales more effectively to larger models, while avoiding the excessive costs of end-to-end ZKML. The modular design of zk-OPML further enables future extensions with the latest advances in the field of ZK.
Open access
Adversarial Robustness in Machine Learning
Physical Unclonable Functions (PUFs) and Hardware Security
Abstract We propose federated quantum randomness with client-side sanity (FQR-CSS), a federated architecture that supplies continuously verifiable quantum entropy to cloud hardware security modules (HSMs) and key management services (KMS). In FQR-CSS, each quantum random number generator (QRNG) node emits a randomness contribution along with a post-quantum zero-knowledge proof (ZKP) attesting to device-level operational predicates. An aggregation layer verifies these proofs, runs Byzantine fault tolerance (BFT) consensus (instantiated via HotStuff) over accepted contributions, and publishes a mixed output with an integrity token. We introduce the security notion of verifiable quantum randomness (VQR), comprising unpredictability, quantum-origin guarantee, and federated integrity. We prove VQR under concrete post-quantum cryptographic assumptions. Our proofs utilize Track-A constructions (ZKPs over classical measurement logs), which are fully implementable today. We further outline a theoretical roadmap for Track-B (direct quantum state verification) to guide future research directions. Our empirical evaluation of a post-quantum zk-STARK (Track-A) demonstrates prover latencies of approximately 26 ms for synthetic statistical predicates (K=1024), with sub-millisecond verification times, proof approximately 2.6 KB, and an estimated end-to-end WAN+HotStuff latency approximately 396 ms in our conservative model.
Open access
Cryptography and Data Security
Physical Unclonable Functions (PUFs) and Hardware Security
The "Identity Trilemma" posits that a decentralized network can enforce only two of the following three properties: Privacy (Anonymity), Accountability (Sybil Resistance), and Permissionlessness (No Central Gatekeeper). Traditional Web2 platforms resolve this by sacrificing Privacy (enforcing Real-Name Policies), while early Web3 platforms sacrificed Accountability, resulting in "Sybil Swarms" where single actors control thousands of wallets. This paper introduces the Klyrox solution to the trilemma: Pseudonymous Accountability. By utilizing Zero-Knowledge Proofs (ZKPs) and non-linear Time-Energy Cost Functions, the Klyrox Protocol enables users to mathematically prove they are unique, high-integrity actors without ever revealing their physical identity, biometric data, or government credentials. We define a new standard for "Proof of Personhood" based not on biology, but on consistent historical behavior recorded in a Soulbound Token (ERC-721M). Author's Note: This paper is a foundational pillar of the Klyrox Protocol architecture, expanding upon the core framework published in The Klyrox Protocol: A Decentralized Framework for Optimistic Content Verification and Epistemic Reputation (available at: https://doi.org/10.5281/zenodo.18729968). It outlines the specific mechanics underpinning the concept of "Epistemic Capital," as explored in the complete five-volume series, The Algorithmic Monographs (The Algorithmic Invisible Hand, The Republic of Code, The Market for Truth, The Heavy Metal Intelligence, and The Synthetic C-Suite).
Exponentially growing data generated by networked devices in Industry 4.0 environments requires industrial analytics that are secure, scalable, and decentralized. This article proposes TADDA-4i, a new multi-layered architecture based on IOTA's Tangle-Directed Acyclic Graph (DAG)-based Distributed Ledger Technology (DLT)-combined with federated learning and edge computing to provide real-time, secure, reliable, and self-sovereign industrial analytics. The architecture minimizes centralized bottlenecks via feeless, asynchronous data validation and tamper-evident model update verification using the Tangle ledger. Adaptive Tip-Aware Data Prioritization (ATDP) and Tangle-Validated Federated Aggregation (TVFA) are two new algorithms proposed for improving responsiveness and securing federated learning integrity. Experimental evaluation in emulated industrial edge environments showed that transactions take 30 percent less time, almost all of the misbehaving updates are detected, the model is about 10 percent more accurate, and output is not reduced even if the number of devices reaches 50. These findings make TADDA-4i an executable solution for the future generations of decentralized industrial intelligence.
The rapid evolution of digital currency systems has consistently faced the fundamental challenge of achieving an optimal balance between transaction privacy, computational efficiency, and cryptographic security. This comprehensive research paper introduces the Elliptic Homomorphic Token (EHT), a groundbreaking cryptographic protocol that revolutionizes privacy-preserving peer-to-peer transactions through the innovative integration of elliptic curve-based partially homomorphic encryption mechanisms and advanced digital signature schemes. Unlike conventional zero-knowledge proof systems that have dominated the privacy-focused cryptocurrency landscape, EHT takes a fundamentally different approach by directly leveraging the underlying cryptographic primitives that form the mathematical foundation of these complex systems. The protocol implements a sophisticated pre-transaction mechanism followed by distributed block recording, achieving remarkable performance metrics of 1000 transactions per second (TPS) with consistently low latency ranging from 50 to 100 milliseconds. Our comprehensive approach systematically addresses the significant computational overhead challenges that were extensively documented during Central Bank Digital Currency (CBDC) implementation projects, while simultaneously providing a robust and practical framework for privacy-preserving digital transactions that maintains the highest standards of cryptographic security. The EHT protocol represents a paradigm shift in how we conceptualize and implement privacy-preserving digital currency systems, offering a more direct, efficient, and mathematically elegant solution compared to existing approaches. Through extensive theoretical analysis, rigorous security proofs, and comprehensive performance evaluations, this paper demonstrates that EHT not only meets but exceeds the requirements for next-generation digital currency systems in terms of privacy, efficiency, scalability, and security.
Sealed-bid auctions are a fundamental mechanism for pricing across a wide range of online applications, but their deployment in online and decentralized environments raises significant security and privacy challenges. This paper presents a comprehensive survey of secure online sealed-bid auction schemes, whose primary contribution lies in a unified taxonomy, a structured comparative analysis, and an explicit identification of design trade-offs and research gaps in secure sealed-bid auction systems. We organize existing work into a taxonomy covering trusted, verifiable, confidential, fully decentralized, and identity-privacyāpreserving auctions and systematically analyze their cryptographic foundations and trust assumptions. Through this comparative evaluation, we highlight fundamental trade-offs between privacy guarantees, public verifiability, and efficiency, particularly the computational cost imposed by zero-knowledge proofs and secure computation techniques. Finally, we identify key research gaps and outline future directions toward scalable, verifiable, and privacy-preserving auction designs for real-world online applications.
Contemporary digital currency systems face fundamental challenges in achieving optimal balance between transaction privacy, computational efficiency, and cryptographic security. While zero-knowledge proof systems have dominated privacy-preserving cryptocurrency research, their practical implementations often involve prohibitive computational overhead that limits real-world deployment. This paper presents a comprehensive analysis of the Elliptic Homomorphic Token (EHT) protocol, which leverages elliptic curve-based partially homomorphic encryption to enable privacy-preserving peer-to-peer transactions without the computational complexity of zero-knowledge constructions. Our theoretical analysis demonstrates strong privacy guarantees under standard cryptographic assumptions, while experimental evaluation shows that EHT achieves 1000 transactions per second with 50-100ms latency. The protocol eliminates the need for complex zero-knowledge proofs by directly utilizing elliptic curve cryptographic primitives, resulting in performance improvements exceeding 100Ć over existing privacy-focused systems while maintaining equivalent security properties.
ABSTRACT The digitization of healthcare insurance claims faces persistent challenges including data breaches, fraudulent submissions, and inefficiencies in verification and settlement. This paper presents a ZeroāKnowledge Succinct NonāInteractive Argument of Knowledge (ZkāSNARK) enabled blockchain framework deployed on the Polygon Proof of Stake (PoS) network for secure and privacyāpreserving health insurance processing. The proposed architecture integrates AttributeāBased Encryption (ABE) for data confidentiality and the Elliptic Curve Digital Signature Algorithm (ECDSA) for authentication, ensuring endātoāend data integrity and access control. Experimental evaluation on the Polygon PoS testbed demonstrates a transaction cost of approximately $0.002, which is over 99% lower than Ethereum's 3ā10 $ per transaction, while maintaining 100% resistance to data tampering, replay attacks, and transaction manipulation. Under the Polygon real network, the proposed framework supports a networkālevel transaction capacity of up to 7000 transactions per second (TPS) under nominal operating conditions, with an approximately 9.3% reduction in effective capacity under stress scenarios, while maintaining 100% verification accuracy for all ZkāSNARK proofs. The average onāchain verification and settlement latency was measured at 4.7 s, confirming the system's suitability for realātime healthcare claim settlement. These results validate that the proposed ZkāSNARK enabled Polygon PoS framework offers a scalable, costāefficient, and cryptographically robust solution for healthcare insurance automation, outperforming existing blockchain implementations across security, efficiency, and economic performance metrics.
Complete technical specification and reference implementation for privacy-preserving payment infrastructure achieving European payment sovereignty while maintaining cryptographic privacy guarantees. This comprehensive study analyzes the Y.I.N. Architectureās DPāZKāHE (Differential Privacy ā Zero-Knowledge ā Homomorphic Encryption) ordering for secure payment settlement. Technical Coverage: The article provides detailed analysis of 43 implementation variants including six cryptographic orderings (with mathematical proofs of security properties), seven zero-knowledge protocols (Sigma, Bulletproofs, STARKs, zk-SNARKs, PLONK, Halo 2, Recursive SNARKs), six homomorphic encryption schemes (CKKS, BFV, TFHE, Multi-key HE, FSS, Garbled Circuits), five differential privacy mechanisms, four deployment architectures, three hardware acceleration approaches, three cross-border payment protocols, three quantum-resistant key management methods, three presentation attack detection techniques, and three accessibility compliance pathways. Implementation & Performance: Includes 2,346 lines of production-ready code with comprehensive error handling, constant-time cryptographic operations, and replay attack protection. Performance benchmarks demonstrate 234ms settlement latency, 640Ć timing attack resistance, and 135Ć adversarial detection capability, suitable for real-time payment processing at scale. Production Deployment: Features complete deployment guides including centralized server architecture, network security configurations, production monitoring with Prometheus metrics, extensive test suite covering honest/tampered/replay scenarios, and enterprise integration strategies for financial institutions and consulting firms. Regulatory Compliance: Comprehensive mapping to 13 global regulations (GDPR, DORA, PSD2, 5AMLD, BSA/AML, CCPA, BIPA, PDPA, PIPL, POPIA, LGPD) and 7 industry standards (PCI DSS, ISO 20022, FIPS 140-3, EMVCo), demonstrating privacy-by-design compliance for digital payment infrastructure. Applications: Reference implementation for European Payments Initiative (EPI), digital euro deployment, sovereign payment networks, cross-border settlement systems, and CBDC infrastructure requiring cryptographic privacy guarantees with regulatory compliance.
Abstract Can a dealer share a secret without knowing the shareholders? We provide a positive answer to this question by introducing the concept of an attribute-based secret sharing (AB-SS) scheme.With AB-SS, a dealer can distribute a secret based on attributes rather than specific individuals or shareholders. Only authorized users whose attributes satisfy a given access structure can recover the secret. Furthermore, we introduce the concept of attribute-based publicly verifiable secret sharing (AB-PVSS). An AB-PVSS scheme allows external users to verify the correctness of all broadcast messages from the dealer and shareholders, similar to a traditional PVSS scheme. Additionally, AB-SS (or AB-PVSS) distinguishes itself from traditional SS (or PVSS) by enabling a dealer to generate shares according to an arbitrary monotone access structure.To build an AB-PVSS scheme, we first implement a decentralized ciphertext-policy attribute-based encryption (CP-ABE) scheme, though not a fully-fledged one.We then incorporate non-interactive zero-knowledge (NIZK) proofs to enable public verification of the CP-ABE ciphertext. Based on the CP-ABE and NIZK proofs, we construct an AB-PVSS primitive.Finally, we conduct security analysis and comprehensive experiments on the proposed CP-ABE and AB-PVSS schemes. The results demonstrate that both schemes exhibit plausible performance compared to related works.
Joshua Edward Mamza, Idris Ismaila, Joseph A. Ojeniyi, Shafiāi Abdulhamid Ā· 6 authors
The Common Vulnerability Scoring System (CVSS) depends on reliable vulnerability data from expert, but the current process of vulnerability score generation and transmission remain exposed to data manipulation and interception. Existing research work used supervised machine learning to automate CVSS scoring with up to 90% accuracy, but their plaintext-based approach lacked cryptographic protections, leaving it vulnerable to Man-in-the-Middle (MitM) attacks. Another research work introduced a homomorphic encryption-based framework that preserves data confidentiality during computation and offers moderate performance gains. However, their dependance on a single trusted aggregator, static key management, and absence of dynamic integrity threshold mechanisms left the system exposed if the aggregatorās key or channel were compromised. An architectural framework for an Enhanced Multi-Party Fully Homomorphic Encryption Scheme (EMHES) was designed to combat Man-in-the-Middle (MitM) attacks targeting Vulnerability Score manipulation. By employing Homomorphic Encryption, the framework enables computations on encrypted vulnerability scores, ensuring confidentiality throughout their lifecycle. Key enhancements include integrating digital signatures to authenticate classified scores before encrypted transmission to cloud environments and verify the integrity of decrypted results post-processing. Digital signatures and regulatory oversight significantly strengthen security properties like non-repudiation, integrity, and confidentiality for cloud-based data computations. The EMHES architecture features a secure transmission channel with multiple security layers within the cloud service provider infrastructure. Additional security mechanisms include secure key management protocols, zero-knowledge proofs for integrity verification, and a resilient secure aggregation protocol designed to counter MitM attacks. From a computational analysis, baseline algorithms exhibit constant time complexity O(1), while the EMHES architecture operates with linear time complexity O(n). The result shows that EMHES provides superior security, integrity and performance on large datasets.
To prevent the reprocessing of the same trade message in different distributed financial infrastructures, electronic trading systems must have powerful duplicate trade detection protocols. Redundant messages are a result of network timeouts, TCP retransmission protocols, upstream retry queues, and manual resubmission workflows that are part of heterogeneous trading structures. Idempotency models define message uniqueness by using composite business keys, cryptographic fingerprints using the SHA-256 hashing functions, and deduplication logic on time windows that trades off between accuracy of detection and scalability of computation. Graphed graph frameworks are enhanced with blockchain and deliver distributed data models to specify intricate trade relations in the form of immutable ledger records, smart contract validation logic, and multi-channel designs, which assure information integrity across trading networks. Multi-channel correlation algorithms differentiate between actual trade amendments and replay events based on machine learning classification models and partial fill cases and cross-venue execution strategies. Strategies of implementation are used to optimize parameters of tolerance windows with the use of hierarchical composite key matching, progressive sampled indexing, and container-based pre-fetching strategies. Microsecond-latency duplicate-detection In-memory caching architectures in conjunction with Bloom filter probabilistic structures can achieve duplicate detection at millions of trade messages per day to protect downstream risk management and regulatory reporting systems against position inflation and compliance violations.
Wyatt Benno, Alberto Centelles, Antoine Douchet, Khalil Gibran
We present Jolt Atlas, a zero-knowledge machine learning (zkML) framework that extends the Jolt proving system to model inference. Unlike zkVMs (zero-knowledge virtual machines), which emulate CPU instruction execution, Jolt Atlas adapts Jolt's lookup-centric approach and applies it directly to ONNX tensor operations. The ONNX computational model eliminates the need for CPU registers and simplifies memory consistency verification. In addition, ONNX is an open-source, portable format, which makes it easy to share and deploy models across different frameworks, hardware platforms, and runtime environments without requiring framework-specific conversions. Our lookup arguments, which use sumcheck protocol, are well-suited for non-linear functions -- key building blocks in modern ML. We apply optimisations such as neural teleportation to reduce the size of lookup tables while preserving model accuracy, as well as several tensor-level verification optimisations detailed in this paper. We demonstrate that Jolt Atlas can prove model inference in memory-constrained environments -- a prover property commonly referred to as \textit{streaming}. Furthermore, we discuss how Jolt Atlas achieves zero-knowledge through the BlindFold technique, as introduced in Vega. In contrast to existing zkML frameworks, we show practical proving times for classification, embedding, automated reasoning, and small language models. Jolt Atlas enables cryptographic verification that can be run on-device, without specialised hardware. The resulting proofs are succinctly verifiable. This makes Jolt Atlas well-suited for privacy-centric and adversarial environments. In a companion work, we outline various use cases of Jolt Atlas, including how it serves as guardrails in agentic commerce and for trustless AI context (often referred to as \textit{AI memory}).
Suvradip Chakraborty, James Hulett, Dakshita Khurana, Kabir Tomer
A recent breakthrough [Hirahara and Nanashima, STOC'2024] established that if $\mathsf{NP} \not \subseteq \mathsf{ioP/poly}$, the existence of zero-knowledge with negligible errors for $\mathsf{NP}$ implies the existence of one-way functions (OWFs). In this work, we obtain a characterization of one-way functions from the worst-case complexity of zero-knowledge {\em in the high-error regime}. We say that a zero-knowledge argument is {\em non-trivial} if the sum of its completeness, soundness and zero-knowledge errors is bounded away from $1$. Our results are as follows, assuming $\mathsf{NP} \not \subseteq \mathsf{ioP/poly}$: 1. {\em Non-trivial} Non-Interactive ZK (NIZK) arguments for $\mathsf{NP}$ imply the existence of OWFs. Using known amplification techniques, this result also provides an unconditional transformation from weak to standard NIZK proofs for all meaningful error parameters. 2. We also generalize to the interactive setting: {\em Non-trivial} constant-round public-coin zero-knowledge arguments for $\mathsf{NP}$ imply the existence of OWFs, and therefore also (standard) four-message zero-knowledge arguments for $\mathsf{NP}$. Prior to this work, one-way functions could be obtained from NIZKs that had constant zero-knowledge error $Īµ_{zk}$ and soundness error $Īµ_{s}$ satisfying $Īµ_{zk} + \sqrt{Īµ_{s}} < 1$ [Chakraborty, Hulett and Khurana, CRYPTO'2025]. However, the regime where $Īµ_{zk} + \sqrt{Īµ_{s}} \geq 1$ remained open. This work closes the gap, and obtains new implications in the interactive setting. Our results and techniques could be useful stepping stones in the quest to construct one-way functions from worst-case hardness.