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Balancing Privacy and Compliance in DeFi: A Zero-Knowledge-Based Auditable Cross-Chain Framework
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arXiv:2608.15276v2 Announce Type: replace Abstract: The rapid emergence of decentralized finance (DeFi) has introduced complex challenges for cross-chain transactions, which involve transferring assets across disparate blockchain networks. A fundamental dilemma arises between user privacy and ensuring regulatory compliance. Unlike single-chain systems, cross-chain environments must address privacy and auditability across heterogeneous architectures. Existing approaches,ranging from transparent ledgers to anonymous cryptocurrencies, fail to reconcile these two requirements, hindering regulatory adoption. This research presents an auditable cross-chain framework that integrates three core components. First, zero-knowledge proofs (ZKPs) enable compliance verification (e.g., amount non-negativity, signature validity) without revealing transaction details. Second, a light-client mechanism enables trust-minimized cross-chain verification without dependence on third-party relayers. Third, a threshold view-key mechanism based on distributed key generation (DKG) restricts that audit access to authorized entities under legal triggers such as the FATF Travel Rule and MiCA Regulation. For cross-border investigations, the framework adheres to national laws and the EU Directive on Mutual Legal Assistance. This work systematically integrates ZKPs, threshold cryptography, and light-client verification into an auditable, privacy-preserving cross-chain protocol, and evaluates a prototype on the Ethereum Sepolia testnet. The results demonstrate the practical feasibility of privacy-preserving compliance verification for cross-chain DeFi. At the same time, broader interoperability and Regulatory Technology (RegTech) impact require further validation on additional chains and with real regulatory workflows.
Source:https://arxiv.org/abs/2608.15276PreviousBeyond Direct Access: Resource Hijacking in LLM AgentsNextPretext: Defeating Malicious Skill Detection Frameworks for AI Agents
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