Verifiable Privacy-Preserving Computing
Tariq Bontekoe, Dimka Karastoyanova, Fatih Turkmen
TL;DR
Verifiable Privacy-Preserving Computation surveys methods that jointly protect data privacy and enable verifiability of computations over distributed data. It provides a taxonomy into four VPPC classes (MPC-based, HE-based, DLT-based, DP-based), formal definitions, and an analysis of efficiency and practicality, based on 37 schemes. The work identifies open challenges such as post-quantum security, modular construction, input authentication, and public verifiability trade-offs, and highlights promising approaches like succinct ZKPs in DLT contexts and MAC-based verifiable HE where public verifiability may be relaxed. The findings inform researchers and practitioners about the landscape, enabling informed choices for secure outsourced and auditable privacy-preserving computations.
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.