The University Daily Kansan
When datasets are distributed on different sources, finding out their intersection while preserving the privacy of the datasets is a widely required task. In this paper, the authors address the Privacy Preserving Set Intersection (PPSI) problem, in which each party learns no elements other than the intersection of the N private datasets. They propose an efficient protocol based on a threshold cryptosystem which is additive homomorphic. The protocol is firstly constructed assuming the adversary is semi-honest and controls arbitrary number of parties and then it's extended to resist the malicious behaviors of the adversary.