Paper 2020/754

Fluid MPC: Secure Multiparty Computation with Dynamic Participants

Abstract

Existing approaches to secure multiparty computation (MPC) require all the participants to commit to the entire duration of the protocol. As interest in MPC continues to grow, it is inevitable that there will be a desire to use it to evaluate increasingly complex functionalities on massive datasets, resulting in computations spanning several hours or days. Such scenarios call for a dynamic participation model for MPC where participants have the flexibility to go offline as needed and (re)join when they have available computational resources. Such a model would also democratize access to privacy-preserving computation by facilitating an ``MPC-as-a-service'' paradigm --- the deployment of MPC in volunteer-operated networks that perform computation on behalf of clients. In this work, we initiate the study of ``fluid MPC'', where parties can dynamically join and leave the computation. The minimum commitment required from each participant is referred to as ``fluidity'', measured in the number of rounds of communication that it must stay online. Our contributions are threefold: 1) We provide a formal treatment of fluid MPC, exploring various possible modeling choices. 2) We construct information-theoretic fluid MPC protocols in the honest-majority setting. Our protocols achieve ``maximal fluidity'', meaning that a party can exit the computation after receiving and sending messages in one round. 3) We implement our protocol and test it in multiple network settings.