A report by the esteemed VC firm a16z states that zero-knowledge proofs (ZK-proofs) and other cryptographic techniques can be used to protect individual privacy while also helping law enforcement combat bad actors in the crypto space
The report was authored by a16z partners Aiden Slaven and regulatory counsel David Sverdlov and published just weeks after Roman Storm was found guilty of conspiracy to operate an unlicensed money transmitting business.
How ZK-Proofs Work to Balance Privacy and Law Enforcement?
Law enforcement and prosecutors argued in the Tornado Cash case that mixing services, which obscure the origin and destination of funds, facilitate criminal activity by allowing bad actors to hide illicit gains.
A16z Crypto’s report proposes a different approach. The report suggests that ZK-proofs could be used at “cash-out points” (e.g., when converting crypto to fiat currency). By furnishing a ZK-proof, a user could provide reasonable assurance to the exchange or financial institution that their crypto did not originate from illicit activities, all while maintaining privacy over their on-chain transactions.
What Are The Broader Uses of ZK-Proofs?
The report highlights that the potential of ZK-proofs extends beyond just financial transactions. They can be used in everyday scenarios where proving a fact without revealing personal information is necessary.
For example, a person could use a ZK-proof to prove their citizenship without having to disclose sensitive documents like a passport or driver’s license, which contains a wealth of private data like address & birth date.
Other Cryptographic Privacy Technologies
A16z Crypto’s report also mentioned other cryptographic techniques that can serve a similar purpose of balancing privacy and security.
- Homomorphic Encryption: This technique allows computations to be performed on encrypted data without first decrypting it. This could allow for data analysis on private information without ever exposing the raw data to the network.
- Multiparty Computation (MPC): MPC allows multiple parties to jointly compute a function over their inputs while keeping those inputs private. This means people could collaborate on a calculation without anyone revealing their individual data to the others.
- Differential Privacy: This method adds statistical noise to data to ensure that aggregated information from surveys or other data collections cannot be used to identify individuals.
Random noise, often drawn from a mathematical distribution (like Laplace or Gaussian), is added to the data or query results. For example, if a survey asks for ages, the reported average might be slightly altered by adding a small random value.
The Case for Privacy-Preserving Technology
The comments from SEC Commissioner Hester Peirce argue that privacy-preserving technologies should be protected and seen as a crucial component of a balanced regulatory framework. They challenge the misconception that privacy on blockchains is inherently a “haven for crime”.
Instead, position technologies like ZK-proofs as a means to achieve both compliance and individual privacy.
