A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing (Extended Abstract)

Shangyu Xie, Meisam Mohammady, Han Wang, Lingyu Wang, Jaideep Vaidya, Yuan Hong

Abstract: In this paper, we propose a prefix-preserving encryption based data outsourcing framework which is applicable to multiple different types of data, such as geo-locations, market basket data, DNA sequences, numerical data and timestamps. It enables accurate data analyses on the encrypted data while ensuring strong privacy against inference attacks. The basic idea is to generates multiple indistinguishable data views in which one view fully preserves the utility for data analysis, and its accurate analysis result can be obliviously retrieved. We empirically evaluate the performance of our outsourcing framework against two common inference attacks on two different real datasets: the check-in location dataset and network traffic dataset, respectively. The experimental results demonstrate that our proposed framework preserves both privacy (with bounded leakage and indistinguishability of data views) and utility.