The PINED-RQ Family: Differentially Private Indexes for Range Query Processing in Clouds

Performing non- aggregate range queries on cloud stored data, while achieving both privacy and efficiency is a challenging problem. With the PINED-RQ family of techniques, we propose constructing a differentially private index to an outsourced encrypted dataset. Efficiency is enabled by using a cleartext index structure to perform range queries. Security relies on both differential privacy (of the index) and semantic security (of the encrypted dataset). Our initial solution, PINED-RQ, develops algorithms for building and updating the differentially private index. Our recent proposals extend PINED-RQ with a parallel architecture for coping with high-rate incoming data. Compared to state-of-the-art secure index based range query processing approaches, PINED-RQ executes queries in the order of at least one magnitude faster. Moreover its parallel extensions increase its throughput by at least one order of magnitude. The security of the PINED-RQ solutions is proved and their efficiency is assessed by extensive experimental validations. In this talk, I will introduce the PINED-RQ family of techniques by presenting the initial PINED-RQ proposal and overviewing then its parallel extensions.