Verifiable Privacy-Preserving Computing

Privacy-enhancing technologies (PETs), such as secure multi-party computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality in computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zero-knowledge proofs (ZKPs) to guarantee (public) verifiability of locally executed computations. We project that applications that are data intensive and require strong privacy guarantees, are also likely to require correctness guarantees. While the combination of methods for (public) verifiability and privacy protection has clear significance, many attempts are far from practical adoption. In this work, we analyze existing solutions that add (public) verifiability to privacy-preserving computations over distributed data, in order to preserve confidentiality and guarantee correctness. To determine the required security and usability properties and whether these are satisfied, we look at various application areas including verifiable outsourcing, distributed ledger technology (DLT), and genomics. We then classify the solutions and describe frequently used approaches as well as efficiency metrics. Last but not least, we identify open challenges and discuss directions for future research that make verifiable, privacy-preserving computations more secure, efficient, and applicable in the real world.