Scalable Mixed-Mode MPC
Protocols for secure multi-party computation (MPC) supporting mixed-mode computation have found a lot of applications in recent years due to their flexibility in representing the function to be evaluated. However, existing mixed-mode MPC protocols are only practical for a small number of parties: they are either tailored to the case of two/three parties or scale poorly for a large number of parties. In this paper, we design and implement a new system for highly efficient and scalable mixed-mode MPC tolerating an arbitrary number of semi-honest corruptions. Our protocols allow secret data to be represented in Encrypted, Boolean, Arithmetic, or Yao form, and support efficient conversions between these representations.
In this work: 1) We design a multi-party table-lookup protocol where both the index and the table can be kept private. The protocol is scalable even with hundreds of parties. 2) Using the above protocol, we design efficient conversions between additive arithmetic secret sharings and Boolean secret sharings for a large number of parties. For 32 parties, our conversion protocols require 1184× to 8141× less communication compared to the state-of-the-art proto-cols MOTION and MP-SPDZ; this leads to up to 1275× improvement in running time under 1 Gbps network. The improvements are even larger with more parties. 3) We also use new protocols to design an efficient multi-party distributed garbling protocol. The protocol could achieve asymptotically constant communication per party.
This is joint work with Kang Yang, Jonathan Katz, and Xiao Wang.
Radhika is a second-year PhD student at Northwestern University, advised by Dr. Xiao Wang. Her interests lie in Applied Cryptography, particularly multiparty computation. She received her B.Tech. in Computer Science from the Indian Institute of Technology Roorkee in 2022.