Efficient data intensive secure computation: Fictional or real?

Abstract

Secure computation has the potential to completely reshape the cybersecruity landscape, but this will happen only if we can make it practical. Despite significant improvements recently, secure computation is still orders of magnitude slower than computation in the clear. Even with the latest technology, running the killer apps, which are often data intensive, in secure computation is still a mission impossible. In this paper, I present two approaches that could lead to practical data intensive secure computation. The first approach is by designing data structures. Traditionally, data structures have been widely used in computer science to improve performance of computation. However, in secure computation they have been largely overlooked in the past. I will show that data structures could be effective performance boosters in secure computation. Another approach is by using fully homomorphic encryption (FHE). A common belief is that FHE is too inefficient to have any practical applications for the time being. Contrary to this common belief, I will show that in some cases FHE can actually lead to very efficient secure computation protocols. This is due to the high degree of internal parallelism in recent FHE schemes. The two approaches are explained with Private Set Intersection (PSI) as an example. I will also show the performance figures measured from prototype implementations.