Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

Abstract

Recently, privacy preservation has become one of the key issues in data mining. In many data mining applications, computing frequencies of values or tuples of values in a data set is a fundamental operation repeatedly used. Within the context of privacy preserving data mining, several privacy preserving frequency mining solutions have been proposed. These solutions are crucial steps in many privacy preserving data mining tasks. Each solution was provided for a particular distributed data scenario. In this paper, we consider privacy preserving frequency mining in a so-called 2-part fully distributed setting. In this scenario, the dataset is distributed across a large number of users in which each record is owned by two different users, one user only knows the values for a subset of attributes, while the other knows the values for the remaining attributes. A miner aims to compute the frequencies of values or tuples of values while preserving each user's privacy. Some solutions based on randomization techniques can address this problem, but suffer from the tradeoff between privacy and accuracy. We develop a cryptographic protocol for privacy preserving frequency mining, which ensures each user's privacy without loss of accuracy. The experimental results show that our protocol is efficient as well.