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A Differentially Private Index for Range Query Processing in Clouds

Cetin Sahin 1 Tristan Allard 2 Reza Akbarinia 3 Amr El Abbadi 1 Esther Pacitti 3 
3 ZENITH - Scientific Data Management
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier, CRISAM - Inria Sophia Antipolis - Méditerranée
Abstract : 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.
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Submitted on : Wednesday, October 3, 2018 - 10:45:01 AM
Last modification on : Friday, August 5, 2022 - 3:03:28 PM
Long-term archiving on: : Friday, January 4, 2019 - 1:14:04 PM


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Cetin Sahin, Tristan Allard, Reza Akbarinia, Amr El Abbadi, Esther Pacitti. A Differentially Private Index for Range Query Processing in Clouds. 34th IEEE International Conference on Data Engineering (ICDE), Apr 2018, Paris, France. pp.857-868, ⟨10.1109/ICDE.2018.00082⟩. ⟨lirmm-01886725⟩



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