Privacy is one of the essential pillars for the widespread adoption of blockchains, but public blockchains are transparentby nature. Modern analytics techniques can easily subduethe pseudonymity feature of a blockchain user. Some applications have been able to provide practical privacy protectionsusing privacy-preserving cryptography techniques. However, malicious actors have abused them illicitly, discouraging honest actors from using privacy-preserving applicationsas "mixing" user interactions and funds with anonymous badactors, causing compliance and regulatory concerns.In this paper, we propose a framework that balances privacypreserving features by establishing a regulatory and compliant framework called Selective De-Anonymization (SeDe).The adoption of this framework allows privacy-preservingapplications on blockchains to de-anonymize illicit transactions by recursive traversal of subgraphs of linked transactions. Our technique achieves this without leaving deanonymization decisions or control in the hands of a singleentity but distributing it among multiple entities while holding them accountable for their respective actions. To instantiate, our framework uses threshold encryption schemes andZero-Knowledge Proofs (ZKPs).