Towards fair mutual private set intersection with linear complexity

Mutual Private Set Intersection with Linear Complexity

A private set intersection (PSI) protocol allows players to obtain the intersection of their inputs. While in its unilateral version only the client can obtain the intersection, the mutual PSI protocol enables all players to get the desired result. In this work, we construct a mutual PSI protocol that is significantly more efficient than the stateof-the-art in the computation overhead. To the b...

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 exis...

Linear-Complexity Private Set Intersection Protocols Secure in Malicious Model

Private Set Intersection (PSI) protocols allow one party (“client”) to compute an intersection of its input set with that of another party (“server”), such that the client learns nothing other than the set intersection and the server learns nothing beyond client input size. Prior work yielded a range of PSI protocols secure under different cryptographic assumptions. Protocols operating in the s...

Fair Private Set Intersection with a Semi-trusted Arbiter

A private set intersection (PSI) protocol allows two parties to compute the intersection of their input sets privately. Most of the previous PSI protocols only output the result to one party and the other party gets nothing from running the protocols. However, a mutual PSI protocol in which both parties can get the output is highly desirable in many applications. A major obstacle in designing a...

A Fair and Efficient Mutual Private Set Intersection Protocol from a Two-Way Oblivious Pseudorandom Function