Verifiable Privacy-Preserving Computing

TL;DR

This paper analyzes and compares 37 schemes that combine verifiability with privacy-preserving computation methods like MPC, HE, and ZKPs, highlighting challenges and future directions for practical deployment.

Contribution

It provides a comprehensive classification and comparison of existing solutions that integrate verifiability with privacy-preserving computations over distributed data.

Findings

Identified key approaches and their security and efficiency trade-offs.

Highlighted open challenges for practical adoption.

Discussed promising future research directions.

Abstract

Privacy-preserving computation (PPC) methods, such as secure multiparty 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 verifiable correctness guarantees, especially when outsourced. While the combination of methods for verifiability and privacy protection has clear benefits, 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 37 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.