ZKPROV: a Zero-Knowledge Approach to Dataset Provenance for Large Language Models

As the deployment of large language models LLMs grows in sensitive domains, ensuring the integrity of their computational provenance becomes a critical challenge, particularly in regulated sectors such as healthcare, where strict requirements are applied in dataset usage. We introduce ZKPROV, a...

CVE-2025-52884 risc0-ethereum-contracts allows invalid commitment with digest value of zero to be accepted by Steel.validateCommitment

RISC Zero is a zero-knowledge verifiable general computing platform, with Ethereum integration. The risc0-ethereum repository contains Solidity verifier contracts, Steel EVM view call library, and supporting code. Prior to versions 2.1.1 and 2.2.0, the Steel.validateCommitment Solidity library...

Verifiable Unlearning on Edge

Machine learning providers commonly distribute global models to edge devices, which subsequently personalize these models using local data. However, issues such as copyright infringements, biases, or regulatory requirements may require the verifiable removal of certain data samples across all edg...

ZK-SERIES: Privacy-Preserving Authentication Using Temporal Biometric Data

Biometric authentication relies on physiological or behavioral traits that are inherent to a user, making them difficult to lose, forge or forget. Biometric data with a temporal component enable the following authentication protocol: recent readings of the underlying biometrics are encoded as tim...

Yotta: a Large-Scale Trustless Data Trading Scheme for Blockchain System

Data trading is one of the key focuses of Web 3.0. However, all the current methods that rely on blockchain-based smart contracts for data exchange cannot support large-scale data trading while ensuring data security, which falls short of fulfilling the spirit of Web 3.0. Even worse, there is...

Fair Data Exchange with Constant-Time Proofs

The Fair Data Exchange FDE protocol introduced at CCS 2024 offers atomic pay-per-file transfers with constant-size proofs, but its prover and verifier runtimes still scale linearly with the file length n. We collapse these costs to essentially constant by viewing the file as a rate-1 Reed-Solomon...

On Immutable Memory Systems for Artificial Agents: a Blockchain-Indexed Automata-Theoretic Framework Using ECDH-Keyed Merkle Chains

This paper presents a formalized architecture for synthetic agents designed to retain immutable memory, verifiable reasoning, and constrained epistemic growth. Traditional AI systems rely on mutable, opaque statistical models prone to epistemic drift and historical revisionism. In contrast, we...

Zero-Knowledge Proof-Of-Location Protocols for Vehicle Subsidies and Taxation Compliance

This paper introduces a new set of privacy-preserving mechanisms for verifying compliance with location-based policies for vehicle taxation, or for electric vehicle EV subsidies, using Zero-Knowledge Proofs ZKPs. We present the design and evaluation of a Zero-Knowledge Proof-of-Location ZK-PoL...

Emission Impossible: Privacy-Preserving Carbon Emissions Claims

Information and Communication Technologies ICT have a significant climate impact, and data centres account for a large proportion of the carbon emissions from ICT. To achieve sustainability goals, it is important that all parties involved in ICT supply chains can track and share accurate carbon...

Applications of Zero-Knowledge Proofs on Bitcoin

This paper explores how zero-knowledge proofs can enhance Bitcoin's functionality and privacy. First, we consider Proof-of-Reserve schemes: by using zk-STARKs, a custodian can prove its Bitcoin holdings are more than a predefined threshold X, without revealing addresses or actual balances. We...

Computational Attestations of Polynomial Integrity Towards Verifiable Machine-Learning

Machine-learning systems continue to advance at a rapid pace, demonstrating remarkable utility in various fields and disciplines. As these systems continue to grow in size and complexity, a nascent industry is emerging which aims to bring machine-learning-as-a-service MLaaS to market. Outsourcing...

Hybrid Stabilization Protocol for Cross-Chain Digital Assets Using Adaptor Signatures and AI-Driven Arbitrage

Stablecoins face an unresolved trilemma of balancing decentralization, stability, and regulatory compliance. We present a hybrid stabilization protocol that combines crypto-collateralized reserves, algorithmic futures contracts, and cross-chain liquidity pools to achieve robust price adherence...

Scaling DeFi with ZK Rollups: Design, Deployment, and Evaluation of a Real-Time Proof-Of-Concept

Ethereum's scalability limitations pose significant challenges for the adoption of decentralized applications dApps. Zero-Knowledge Rollups ZK Rollups present a promising solution, bundling transactions off-chain and submitting validity proofs on-chain to enhance throughput and efficiency. In thi...

Confidential Guardian: Cryptographically Prohibiting the Abuse of Model Abstention

Cautious predictions -- where a machine learning model abstains when uncertain -- are crucial for limiting harmful errors in safety-critical applications. In this work, we identify a novel threat: a dishonest institution can exploit these mechanisms to discriminate or unjustly deny services under...

DP-RTFL: Differentially Private Resilient Temporal Federated Learning for Trustworthy AI in Regulated Industries

Federated Learning FL has emerged as a critical paradigm for enabling privacy-preserving machine learning, particularly in regulated sectors such as finance and healthcare. However, standard FL strategies often encounter significant operational challenges related to fault tolerance, system...

TeleSparse: Practical Privacy-Preserving Verification of Deep Neural Networks

Verification of the integrity of deep learning inference is crucial for understanding whether a model is being applied correctly. However, such verification typically requires access to model weights and potentially sensitive or private training data. So-called Zero-knowledge Succinct...

Engineering Trustworthy Machine-Learning Operations with Zero-Knowledge Proofs

As Artificial Intelligence AI systems, particularly those based on machine learning ML, become integral to high-stakes applications, their probabilistic and opaque nature poses significant challenges to traditional verification and validation methods. These challenges are exacerbated in regulated...

CVE-2024-38533

ZKsync Era is a layer 2 rollup that uses zero-knowledge proofs to scale Ethereum. There is possible invalid stack access due to the addresses used to access the stack not properly being converted to cells. This issue has been patched in version 1.5.0...

CVE-2024-45040

gnark is a fast zk-SNARK library that offers a high-level API to design circuits. Prior to version 0.11.0, commitments to private witnesses in Groth16 as implemented break the zero-knowledge property. The vulnerability affects only Groth16 proofs with commitments. Notably, PLONK proofs are not...

CVE-2022-29566

The Bulletproofs 2017/1066 paper mishandles Fiat-Shamir generation because the hash computation fails to include all of the public values from the Zero Knowledge proof statement as well as all of the public values computed in the proof, aka the Frozen Heart issue...