MLPSI: Multi-party Privacy Set Intersection with Linear Complexity
Benxin Yin, Hansong Xu, Xinliang Li, Jun Sun, Hanlin Zhang · 2022
Multi-party privacy set intersectionenables multiple parties to compute the intersection of their datasets without leaking the data privacy. Among existing MPSI protocols, practical multi-party private set intersection from symmetric-key techniques(KMPRT) is a paradigm protocol leveraging the oblivious evaluation of programmable pseudorandom function (OPPRF). OPPRF or oblivious pseudorandom function (OPRF) are usually used to generate pseudorandom numbers, which require a lot of online interactions. To avoid such frequent interactions, this paper propose a new protocol named Privacy Set Intersection with Linear Complexity (MLPSI). The MLPSI integrates a Zero Sharing (ZS) and Garbled Bloomfilter(GBF), which will significantly reduces the communication and computing overhead.