Verifiability for privacy-preserving computing on distributed data — a survey

Abstract

Privacy-preserving computation (PPC) solutions, such as secure multiparty computation (MPC) and homomorphic encryption (HE), are deployed increasingly often to guarantee data confidentiality for computations over private, distributed data. Similarly, we observe a steep increase in the adoption of zeroknowledge 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 verifiable correctness guarantees, especially when they are outsourced. Combining methods for verifiability and privacy protection has clear benefits, however certain challenges stand before their widespread practical adoption. In this work, we analyze existing solutions that combine verifiability with privacy-preserving computations over distributed data, in order to preserve confidentiality and guarantee correctness at the same time. We classify and compare 41 different schemes, regarding solution approach, security, efficiency, and practicality. Lastly, we discuss some of the most promising solutions in this regard, and present various open challenges and directions for future research.