Privacy Preserving Frequency Mining in 2-Part Fully Distributed Setting

Abstract

The Dung LUONG †a) , Nonmember and Tu Bao HO † †b) , Member SUMMARYRecently, 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. key words: privacy preserving frequency mining, 2-part fully distributed setting, cryptography

Privacy Preserving Classification in Two-Dimension Distributed Data

“…The methods based on perturbation (e.g., [1], [3], [23]) have been proved to be efficient, but have a tradeoff between privacy and accuracy. The methods based on cryptography (e.g., [22], [20], [11]) can safely preserve privacy without loss of accuracy, but have high complexity and high communication cost. These privacy preserving data mining methods have been presented for various scenarios in which the general idea is to allow mining datasets distributed across multiple parties, without disclosing each party's private data [2].…”