A Utility-Aware and Holistic Approach for Privacy Preserving Distributed Mining with Worst Case Privacy Guarantee

Abstract

Organizations often want to predict some attribute values collaboratively. However, they are often unwilling or not allowed to directly share their private data. Thus there is great need for distributed privacy preserving techniques. There exists a rich body of work based on Secure MultiParty Computation techniques. However, most such techniques are tied to a specific mining algorithm and users have to run a different protocol for each mining algorithm. A holistic approach was proposed in which all parties first use a SMC protocol to generate a synthetic data set and then share this data for different mining algorithms. However, this approach has two major drawbacks: 1) it provides no worst case privacy guarantee, 2) parties involved in the mining process often know what attribute to predict, but the holistic approach does not take this into account. In this paper, we propose a method that addresses these shortcomings. Experimental results demonstrate the benefits of the proposed solution.