OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain

Journal article


Zeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Naeem Ramzan and Wajahat Ali Khan 2017. OS2: Oblivious similarity based searching for encrypted data outsourced to an untrusted domain. PLos ONE. 12 (7), pp. 1-22. https://doi.org/10.1371/journal.pone.0179720
AuthorsZeeshan Pervez, Mahmood Ahmad, Asad Masood Khattak, Naeem Ramzan and Wajahat Ali Khan
Abstract

Public cloud storage services are becoming prevalent and myriad data sharing, archiving and collaborative services have emerged which harness the pay-as-you-go business model of public cloud. To ensure privacy and confidentiality often encrypted data is outsourced to such services, which further complicates the process of accessing relevant data by using search queries. Search over encrypted data schemes solve this problem by exploiting cryptographic primitives and secure indexing to identify outsourced data that satisfy the search criteria. Almost all of these schemes rely on exact matching between the encrypted data and search criteria. A few schemes which extend the notion of exact matching to similarity based search, lack realism as those schemes rely on trusted third parties or due to increase storage and computational complexity. In this paper we propose Oblivious Similarity based Search () for encrypted data. It enables authorized users to model their own encrypted search queries which are resilient to typographical errors. Unlike conventional methodologies, ranks the search results by using similarity measure offering a better search experience than exact matching. It utilizes encrypted bloom filter and probabilistic homomorphic encryption to enable authorized users to access relevant data without revealing results of search query evaluation process to the untrusted cloud service provider. Encrypted bloom filter based search enables to reduce search space to potentially relevant encrypted data avoiding unnecessary computation on public cloud. The efficacy of is evaluated on Google App Engine for various bloom filter lengths on different cloud configurations.

KeywordsPublic cloud storage ; data sharing; probabilistic homomorphic encryption
Year2017
JournalPLos ONE
Journal citation12 (7), pp. 1-22
PublisherPublic Library of Science (PLoS)
ISSN1932-6203
Digital Object Identifier (DOI)https://doi.org/10.1371/journal.pone.0179720
Web address (URL)https://doi.org/10.1371/journal.pone.0179720
Output statusPublished
Publication dates10 Jul 2017
Publication process dates
Deposited21 Aug 2024
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