Secure multi-party computation problems and their applications

The growth of the Internet has triggered tremendous opportunities for cooperative computation, where people are jointly conducting computation tasks based on the private inputs they each supplies. These computations could occur between mutually untrusted parties, or even between competitors. For example, customers might send to a remote database queries that contain private information; two competing financial organizations might jointly invest in a project that must satisfy both organizations' private and valuable constraints, and so on. Today, to conduct such computations, one entity must usually know the inputs from all the participants; however if nobody can be trusted enough to know all the inputs, privacy will become a primary concern.This problem is referred to as Secure Multi-party Computation Problem (SMC) in the literature. Research in the SMC area has been focusing on only a limited set of specific SMC problems, while privacy concerned cooperative computations call for SMC studies in a variety of computation domains. Before we can study the problems, we need to identify and define the specific SMC problems for those computation domains. We have developed a framework to facilitate this problem-discovery task. Based on our framework, we have identified and defined a number of new SMC problems for a spectrum of computation domains. Those problems include privacy-preserving database query, privacy-preserving scientific computations, privacy-preserving intrusion detection, privacy-preserving statistical analysis, privacy-preserving geometric computations, and privacy-preserving data mining.The goal of this paper is not only to present our results, but also to serve as a guideline so other people can identify useful SMC problems in their own computation domains.