A Differentially Private Index for Range Query Processing in Clouds

• Published in: 2018 IEEE 34th International Conference on Data Engineering (ICDE) pp. 857 - 868
• Main Authors: Sahin, Cetin, Allard, Tristan, Akbarinia, Reza, El Abbadi, Amr, Pacitti, Esther
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2018
• Subjects: Cryptography; Data structures; Differential Privacy; Differentially Private Index; Indexes; Outsourcing to Cloud; Privacy; Privacy preserving; Querying encrypted data; Range Query
• ISSN: 2375-026X
• DOI: 10.1109/ICDE.2018.00082

Performing non-aggregate range queries on cloud stored data, while achieving both privacy and efficiency is a challenging problem. This paper proposes 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 solution, PINED-RQ develops algorithms for building and updating the differentially private index. 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. The security of PINED-RQ is proved and its efficiency is assessed by an extensive experimental validation.