An Efficient Privacy-Preserving Protocol for Computing kth Minimum Value in P2P Networks

Abstract

Statistics such as [Formula: see text]th minimum value play a crucial role in our data-driven society, for example by informing decision-making. In this paper, we propose an efficient privacy-preserving protocol that allows a group of users who do not trust each other, for example in a peer-to-peer (P2P) network, to jointly calculate the [Formula: see text]th minimum value. Specifically, in our proposed protocol each user’s data is converted to a binary bit string following a certain rule. Then, the bits at the same position are aggregated from the leftmost to the rightmost. As far as we know, this is the first published scheme to obtain [Formula: see text]th minimum value in a P2P network without affecting users’ privacy. We also remark that the proposed protocol can be easily generalized to compute other statistics, such as maximum value, minimum value, and median value, while achieving high efficiency in a privacy-preserving P2P network. We then demonstrate that the proposed protocol achieves forward security and is resilient to a range of external and internal attacks.