Practical private range search revisited

Ioannis Demertzis, Stavros Papadopoulos, Odysseas Papapetrou, Antonios Deligiannakis, Minos Garofalakis

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

125 Citations (Scopus)

Abstract

We consider a data owner that outsources its dataset to an untrusted server. The owner wishes to enable the server to answer range queries on a single attribute, without compromising the privacy of the data and the queries. There are several schemes on "practical" private range search (mainly in Databases venues) that attempt to strike a trade-off between efficiency and security. Nevertheless, these methods either lack provable security guarantees, or permit unacceptable privacy leakages. In this paper, we take an interdisciplinary approach, which combines the rigor of Security formulations and proofs with efficient Data Management techniques. We construct a wide set of novel schemes with realistic security/performance trade-offs, adopting the notion of Searchable Symmetric Encryption (SSE) primarily proposed for keyword search. We reduce range search to multi- keyword search using range covering techniques with treelike indexes. We demonstrate that, given any secure SSE scheme, the challenge boils down to (i) formulating leakages that arise from the index structure, and (ii) minimizing false positives incurred by some schemes under heavy data skew. We analytically detail the superiority of our proposals over prior work and experimentally confirm their practicality.

Original languageEnglish
Title of host publicationSIGMOD 2016 - Proceedings of the 2016 International Conference on Management of Data
Place of PublicationNew York
PublisherAssociation for Computing Machinery, Inc
Pages185-198
Number of pages14
ISBN (Electronic)9781450335317
DOIs
Publication statusPublished - 26 Jun 2016
Externally publishedYes
Event2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016 - San Francisco, United States
Duration: 26 Jun 20161 Jul 2016

Conference

Conference2016 ACM SIGMOD International Conference on Management of Data, SIGMOD 2016
Country/TerritoryUnited States
CitySan Francisco
Period26/06/161/07/16

Funding

This work was partially supported by the European Commission under ICT-FP7-LEADS-318809 (Large-Scale Elastic Architecture for Data-as-a-Service) and ICT-FP7-Quali-Master-619525 (A Configurable Real-time Data Processing Infrastructure Mastering Autonomous Quality Adaptation).

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