A Generalized Framework for Preserving Both Privacy and Utility in Data Outsourcing.

Property preserving encryption techniques have significantly advanced the utility of encrypted data in various data outsourcing settings (e.g., the cloud). However, while preserving certain properties (e.g., the prefixes or order of the data) in the encrypted data, such encryption schemes are typically limited to specific data types (e.g., prefix-preserved IP addresses) or applications (e.g., range queries over order-preserved data), and highly vulnerable to the emerging inference attacks which may greatly limit their applications in practice. In this paper, to the best of our knowledge, we make the first attempt to generalize the prefix preserving encryption via encoding that is not only applicable to more general data types (e.g., geo-locations, market basket data, DNA sequences, numerical data and timestamps) but also secure against the inference attacks. Furthermore, we present a generalized framework that generates multiple data views in which one view fully preserves the utility for data analysis, and its accurate analysis result can be obliviously retrieved. Given any specified privacy leakage bound, the computation and communication overheads are minimized to effectively defend against different inference attacks. 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 (with 100% analysis accuracy).