A Differentially Private Index for Range Query Processing in Clouds

Cetin Sahin 1 Tristan Allard 2 Reza Akbarinia 3 Amr Abbadi 1 Esther Pacitti 3
1 Department of Computer Science [Santa Barbara]
2 DRUID - Declarative & Reliable management of Uncertain, user-generated Interlinked Data
IRISA-D7 - GESTION DES DONNÉES ET DE LA CONNAISSANCE
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.
Type de document :
Communication dans un congrès
ICDE: International Conference on Data Engineering, Apr 2018, Paris, France. 34th IEEE International Conference on Data Engineering, 2018, 〈https://icde2018.org〉
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Contributeur : Reza Akbarinia <>
Soumis le : mercredi 3 octobre 2018 - 10:45:01
Dernière modification le : mardi 4 décembre 2018 - 14:32:05

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Cetin Sahin, Tristan Allard, Reza Akbarinia, Amr Abbadi, Esther Pacitti. A Differentially Private Index for Range Query Processing in Clouds. ICDE: International Conference on Data Engineering, Apr 2018, Paris, France. 34th IEEE International Conference on Data Engineering, 2018, 〈https://icde2018.org〉. 〈lirmm-01886725〉

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