%0 Conference Proceedings
%T A Practical Framework for Executing Complex Queries over Encrypted Multimedia Data
%+ University of Texas at Dallas [Richardson] (UT Dallas)
%A Shaon, Fahad
%A Kantarcioglu, Murat
%Z Part 4: Protection and Privacy of Data and Big Data
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 30th IFIP Annual Conference on Data and Applications Security and Privacy (DBSec)
%C Trento, Italy
%Y Silvio Ranise
%Y Vipin Swarup
%I Springer International Publishing
%3 Data and Applications Security and Privacy XXX
%V LNCS-9766
%P 179-195
%8 2016-07-18
%D 2016
%R 10.1007/978-3-319-41483-6_14
%Z Computer Science [cs]Conference papers
%X Over the last few years, data storage in cloud based services has been very popular due to easy management and monetary advantages of cloud computing. Recent developments showed that such data could be leaked due to various attacks. To address some of these attacks, encrypting sensitive data before sending to cloud emerged as an important protection mechanism. If the data is encrypted with traditional techniques, selective retrieval of encrypted data becomes challenging. To address this challenge, efficient searchable encryption schemes have been developed over the years. Almost all of the existing searchable encryption schemes are developed for keyword searches and require running some code on the cloud servers. However, many of the existing cloud storage services (e.g., Dropbox (https://www.dropbox.com), Box (https://www.box.com/), Google Drive (http://drive.google.com/), etc.) only allow simple data object retrieval and do not provide computational support needed to realize most of the searchable encryption schemes.In this paper, we address the problem of efficient execution of complex search queries over wide range of encrypted data types (e.g., image files) without requiring customized computational support from the cloud servers. To this end, we provide an extensible framework for supporting complex search queries over encrypted multimedia data. Before any data is uploaded to the cloud, important features are extracted to support different query types (e.g., extracting facial features to support face recognition queries) and complex queries are converted to series of object retrieval tasks for cloud service. Our results show that this framework may support wide range of image retrieval queries on encrypted data with little overhead and without any change to underlying data storage services.
%G English
%Z TC 11
%Z WG 11.3
%2 https://inria.hal.science/hal-01633678/document
%2 https://inria.hal.science/hal-01633678/file/428203_1_En_14_Chapter.pdf
%L hal-01633678
%U https://inria.hal.science/hal-01633678
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC11
%~ IFIP-WG11-3
%~ IFIP-DBSEC
%~ IFIP-LNCS-9766