Privacy preserving frequency-based learning algorithms in two-part partitioned record model

Abstract

In this paper, we consider a new scenario for privacy-preserving data mining called two-part partitioned record model (TPR) and find solutions for a family of frequency-based learning algorithms in TPR model. In TPR, 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 and the other knows the values for the remaining attributes. A miner aims to learn, for example, classification rules on their data, while preserving each user’s privacy. In this work we develop a cryptographic solution for frequency-based learning methods in TPR. The crucial step in the proposed solution is the privacy-preserving computation of frequencies of a tuple of values in the users’ data, which can ensure each user’s privacy without loss of accuracy.We illustrate the applicability of the method by using it to build the privacy preserving protocol for the naive Bayes classifier learning, and briefly address the solution in other applications. Experimental results show that our protocol is efficient.