What are the key privacy-preserving techniques mentioned in the chapter that help address challenges in blockchain forensics?

The key privacy-preserving techniques mentioned are Zero-Knowledge Proofs (ZKPs), Homomorphic Encryption, Secure Multi-Party Computation (SMPC), and Differential Privacy. These techniques help mitigate risks in blockchain forensics while allowing effective analysis without compromising individual privacy.

Zero-Knowledge Proofs (ZKPs) allow one party to prove the truth of a statement without revealing additional information, which can be used to verify transactions without disclosing sensitive details. Homomorphic Encryption enables computations on encrypted data without needing to decrypt it, allowing forensic investigators to analyze blockchain data while maintaining privacy. Secure Multi-Party Computation (SMPC) allows multiple parties to compute functions over their inputs while keeping those inputs private, which is useful in collaborative investigations. Differential Privacy adds noise to data to protect individual data points while still providing useful insights at the aggregate level, thus preserving privacy in blockchain analytics.