Eos: Efficient Private Delegation of zkSNARK Provers - Ryan Lehmkuhl
Channel: ConsensusLab
Date: 2023-06-14

Alessandro Chiesa (UC Berkeley, EPFL), Ryan Lehmkuhl (MIT), Pratyush Mishra (Aleo, UPenn), Yinuo Zhang (UC Berkeley)

Succinct zero knowledge proofs (i.e. zkSNARKs) are powerful cryptographic tools that enable a prover to convince a verifier that a given statement is true without revealing any additional information. Their attractive privacy properties have led to much academic and industrial interest.Unfortunately, existing systems for generating zkSNARKs are expensive, which limits the applications in which these proofs can be used. One approach is to take advantage of powerful cloud servers to generate the proof. However, existing techniques for this (e.g., DIZK) sacrifice privacy by revealing secret information to the cloud machines. This is problematic for many applications of zkSNARKs, such as decentralized private currency and computation systems. In this work we design and implement privacy-preserving delegation protocols for zkSNARKs with universal setup. Our protocols enable a prover to outsource proof generation to a set of workers, so that if at least one worker does not collude with other workers, no private information is revealed to any worker. Our protocols achieve security against malicious workers without relying on heavyweight cryptographic tools. We implement and evaluate our delegation protocols for a state-of-the-art zkSNARK in a variety of computational and bandwidth settings, and demonstrate that our protocols are concretely efficient. When compared to local proving, using our protocols to delegate proof generation from a recent smartphone (a) reduces end-to-end latency by up to 26×26×, (b) lowers the delegator's active computation time by up to 1447×1447×, and (c) enables proving up to 256×256× larger instances.

ConsensusDays 23 Session 1.1- Consensus Cryptography
Site: https://research.protocol.ai/sites/consensusday23/
Playlist: https://www.youtube.com/playlist?list=PLxN6L-h3wFfsRKLCj4I8xKeKjy9OL-EY6