Practical Private Set Intersection Protocols with Linear Complexity
The constantly increasing dependence on anytime-anywhere availability of data and the commensurately increasing fear of losing
privacy motivate the need for privacy-preserving techniques. One interesting and common problem occurs when two parties need
to privately compute an intersection of their respective sets of data. In doing so, one or both parties must obtain the intersection
(if one exists), while neither should learn anything about other set elements. Although prior work has yielded a number of
effective and elegant Private Set Intersection (PSI) techniques, the quest for efficiency is still underway. This paper explores some PSI variations and constructs several secure protocols that are appreciably more efficient than the state-of-the-art.