Problem
Research questions and friction points this paper is trying to address.
Blockchain
Privacy Protection
Regulatory Compliance
SeDe: Balancing Blockchain Privacy and Regulatory Compliance by Selective De-Anonymization
🤖 AI Summary
Blockchain privacy protection and regulatory compliance exhibit a fundamental tension: strong anonymity impedes illicit transaction detection, while centralized oversight compromises user privacy. To address this, we propose Selective De-anonymization (SeDe), a framework enabling controlled, verifiable de-anonymization exclusively for suspicious transactions. Our contributions are threefold: (1) a novel distributed, accountability-driven de-anonymization mechanism leveraging threshold encryption and zero-knowledge proofs to eliminate single points of trust; (2) precise identification of malicious fund flows via graph-structured analysis and recursive subgraph traversal of on-chain transactions; and (3) dynamic delegation of decision-making authority and audit responsibility across multiple trusted entities. Experiments demonstrate that SeDe enables efficient, cryptographically verifiable compliance auditing while preserving strong privacy guarantees—constituting the first trustless, deployable compliance-enhancement solution for privacy-preserving cryptocurrencies and mixers.
📝 Abstract
Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparent by nature. Modern analytics techniques can easily subdue the pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protections using privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applications as"mixing"user interactions and funds with anonymous bad actors, causing compliance and regulatory concerns. In this paper, we propose a framework that balances privacy-preserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe). The adoption of this framework allows privacy-preserving applications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving de-anonymization decisions or control in the hands of a single entity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes and Zero-Knowledge Proofs (ZKPs).
Innovation
Methods, ideas, or system contributions that make the work stand out.
Selective De-anonymization
Privacy Protection
Zero Knowledge Proofs
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