Data is now collected and processed on tremendous scale. Unfortunately, in several crucial applications it is not possible to leverage the full extent of collected information, e.g. when attempting to identify global properties across sensitive data sets held by agent(s) who are unable or unwilling to share the information.
A promising approach to data sharing is to make use of cryptographic tools such as secure multi-party computation (MPC), which enable mutually untrusting parties to jointly evaluate a function f over their secret inputs, while guaranteeing that information on their inputs will not be revealed beyond the function output.
However, despite great progress in MPC techniques in the last three decades, the surrounding world of data aggregation and computation has leapt even more rapidly forward. For example, nearly all existing MPC protocols require each party to store information comparable to the total combined data, and evaluate the desired function via its circuit representation. When the number of parties and size of data is large, or when the functions to be computed are "lightweight" (e.g. touching only small portions of the data), these limitations completely obliterate feasibility of MPC as a solution.
In this talk, I will introduce techniques yielding MPC protocols whose parameters scale to the modern regime of massive data; in particular, which support efficient secure computation of Parallel RAM programs
Elette Boyle, Technion Israel Institute of Technology