Privacy-preserving statistical computing protocols for private set intersection

With the rapid development of Internet and the widespread application of distributed computing, people enjoy various conveniences while at the same time their privacy has also been threatened. Secure multiparty computation (MPC) can solve the problem of how data owners who do not trust each other jointly compute in distributed scenarios. Using MPC technique, people can not only realize data joint computing, but also ensure data privacy. In most data application scenarios, private data held by different parties can often be represented by sets. To complete the relevant statistical computations of the intersection of two private sets, we propose a suite of protocols based on MPC. These protocols can compute the statistical functions of the associated data of the intersection, including cardinality, sum, average, variance, range, and so forth, without revealing any additional information other than the result. To achieve these functions, we design a private membership test protocol with the result as the arithmetic sharing value, called the arithmetic shared private membership test (ASPMT) protocol. On the basis of the ASPMT protocol, the size and other statistics of the intersection can be computed securely and efficiently. All fundamental computations are constructed based on secret sharing and oblivious transfer techniques. Thanks to the use of precomputation technique, all protocols are highly efficient.