SCALES: MPC with Small Clients and Large Ephemeral Servers
The recently proposed YOSO model is a groundbreaking approach to MPC, executable on a public blockchain, circumventing adaptive player corruption by hiding the corruption targets until they are worthless. Players are selected unpredictably from a large pool to perform MPC subtasks, in which each selected player sends a single message (and reveals their identity). While YOSO MPC has attractive asymptotic complexity, unfortunately, it is concretely prohibitively expensive due to the cost of its building blocks.
We propose a modification to the YOSO model that preserves resilience to adaptive server corruption, but allows for much more efficient protocols. In SCALES (Small Clients And Larger Ephemeral Servers) only the servers facilitating the MPC computation are ephemeral (unpredictably selected and “speak once”). Input providers (clients) publish the problem instance and collect the output, but do not otherwise participate in computation. SCALES offers attractive features, and improves over YOSO in outsourcing MPC to a large pool of servers under adaptive corruption.
We build SCALES from Rerandomizable Garbling Schemes (RGS). RGS is a contribution of independent interest with additional applications.
Anasuya Acharya is currently pursuing a Ph.D. at Bar-Ilan University, advised by Prof. Carmit Hazay. She completed her M.Tech. at IIT Bombay, where she was advised by Prof. Manoj Prabhakaran, and B.Tech. at NIT Tiruchirappalli. Her research interests lie in cryptography and secure multiparty computation, specifically in designing and applying garbling schemes in MPC protocols.