Fast Private Association Rule Mining by A Protocol for Securely Sharing Distributed Data

Abstract

Privacy concerns may discourage users who would otherwise join beneficial data mining tasks for intelligence and/or security. We propose an efficient protocol that allows parties to share data in a private way with no restrictions and without loss of accuracy. Our method has the immediate application that horizontally partitioned databases can be brought together and made public without disclosing the source/owner of each record. At another level, we have an additional benefit that we can apply our protocol to privately discover association rules. Our protocol is more efficient than previous methods. The effects of our protocol are less than others: 1) each party can identify only their data, 2) no party is able to learn the links between other parties and their data, 3) no party learns any transactions of the other parties' databases.