Paper 2020/623

PSI-Stats: Private Set Intersection Protocols Supporting Secure Statistical Functions

Jason H. M. Ying, Shuwei Cao, Geong Sen Poh, Jia Xu, and Hoon Wei Lim

Abstract

Private Set Intersection (PSI) enables two parties, each holding a private set to securely compute their intersection without revealing other information. This paper considers settings of secure statistical computations over PSI, where both parties hold sets containing identifiers with one of the parties having an additional positive integer value associated with each of the identifiers in her set. The main objective is to securely compute some desired statistics of the associated values for which its corresponding identifiers occur in the intersection of the two sets. This is achieved without revealing the identifiers of the set intersection. In this paper, we present protocols which enable the secure computations of statistical functions over PSI, which we collectively termed PSI-Stats. Implementations of our constructions are also carried out based on simulated datasets as well as on actual datasets in the business use cases that we defined, in order to demonstrate practicality of our solution. PSI-Stats incurs 5x less monetary cost compared to the current state-of-the-art circuit-based PSI approach due to Pinkas et al. (EUROCRYPT'19). Our solution is more tailored towards business applications where monetary cost is the primary consideration.

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