Efficiently Computing with Private Data
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Today, individual users and organizations often wish to contribute their private data to compute functions of interest. Unfortunately, when data is deemed too valuable or is legally protected, such computation cannot be performed. Secure Multiparty Computation (MPC) is a subfield of Cryptography that allows mutually untrusting parties to work together to run programs over their private data without revealing any information except the program output. In this way, MPC allows users to share private data while guaranteeing its privacy. One fundamental and efficient MPC technique, Yao’s garbled circuit, represents the computed function as a Boolean circuit, and evaluates it gate-by-gate under encryption. While achieving excellent cost per gate, this approach requires evaluation of the entire circuit. In particular, all inactive conditional branches must be sent over the network and evaluated: While inactive conditional branches are not needed to correctly compute functions, omitting them leaks information about the players’ private inputs. After 35 years of active research, it is generally believed that this cost cannot be avoided without using relatively inefficient tools, such as universal circuits. In this talk, I will discuss a new technique for garbled circuit MPC, which challenges this widely-held belief. I will show how to avoid sending inactive circuit branches at very modest increase of computation. Because of this optimization, communication costs are proportional to the longest execution path, rather than to the entire circuit. The talk will be kept at a high level, and no cryptographic background is required. The presented work is in collaboration with my advisor Vlad Kolesnikov.