International Association
for Cryptologic Research

Privacy is a growing concern for smart contracts on public ledgers. In recent years, we have seen several practical systems for privacy-preserving smart contracts, but they only target privacy of on-chain data, and rely on trusted off-chain parties with user data -- for instance, a decentralized finance application (e.g. exchange) relies on an off-chain matching engine to process client orders that get settled on-chain, where privacy only applies to the on-chain data. Privacy conscious users demand stronger notions of privacy, for their identity and their data, from all other parties in the ecosystem.

We propose a novel framework for smart contracts that ensures {\em doubly private} execution, addressing {both on-chain and off-chain privacy} requirements. In our framework, clients submit their requests in a privacy-preserving manner to a group of (potentially mutually untrusting) servers. These servers collaboratively match client requests without learning any information about the data or identities of the clients.

We then present {\em Jigsaw}, an efficient cryptographic realization of our proposed framework. {\em Jigsaw} builds on the ZEXE architecture (Bowe et al., S\&P 2020), which leverages zkSNARKs, and extends Collaborative zkSNARKs (Ozdemir and Boneh, USENIX 2022) to enable proof generation by a group of servers.

In Jigsaw, we introduce a novel collaborative zkSNARK construction that achieves low latency and reduced proving time, and showcase these advantages over sample applications ranging from trading in a decentralized exchange to auctions and voting. Our experiments demonstrate that {\em Jigsaw} is roughly $40-50$x faster in proof generation and uses orders-of-magnitude less bandwidth than the naive approach of using off-the-shelf Collaborative zkSNARKs.