Browsing by Author "Kechid, Samir"

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    Accelerated Search over Encrypted Cloud Data
    (IEEE, 2017-02-13) Boucenna, Fateh; Nouali, Omar; Dabah, Adel; Kechid, Samir
    Companies and other organizations such as hospitals seek more and more to enjoy the benefits of cloud computingin terms of storage space and computing power. However, outsourced data must be encrypted in order to be protected againstpossible attacks. Therefore, traditional information retrieval systems (IRS) are no longer effective and must be adapted in order towork over encrypted cloud data. In addition, in order to providethe ability to search over an encrypted index, we use the vectormodel to represent documents and queries which is the most usedin the literature. During the search process, the query vectormust be compared with each document vector which is a time consuming process since the data collection is generally huge.Consequently, the search performance is degraded and the searchprocess is too slow. To overcome this drawback, we proposethe use of High Performance Computing (HPC) architecturesto accelerate the search over encrypted cloud data. Indeed,we propose several techniques that take benefit from Graphics Processing Unit (GPU) and computer cluster architectures by distributing the work between different threads. In addition,in order to get the best performance, we design our solutionsso that they can process several queries simultaneously. Theexperimental study using 400.000 documents demonstrates theefficiency of our proposals by reaching a speed-up around 46x.
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    Concept-based Semantic Search over Encrypted Cloud Data
    (2016-04-23) Boucenna, Fateh; Nouali, Omar; Kechid, Samir
    Cloud computing is a technology that allows companies and individuals to outsource their data and their applications. The aim is to take advantage from the power of storage and processing offered by such technology. However, in order to preserve data privacy, it is crucial that all data must be encrypted before being outsourced into the cloud. Moreover, authorized users should be able to recover their outsourced data. This process can be complicated due to the fact that data are encrypted. The traditional information retrieval systems only work over data in the clear. Therefore, dedicated information retrieval systems were developed to deal with the encrypted cloud data. Several kinds of search over cloud data have been proposed in the literature such as Boolean search, multi-keyword ranked search and fuzzy search. However, the semantic search is little addressed in the literature. In this paper, we propose an approach called SSE-S that take into account the semantic search in the cloud by using Wikipedia ontology to understand the meaning of documents and queries with maintaining the security and the privacy issues.
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    Secure Inverted Index Based Search over Encrypted Cloud Data with User Access Rights Management
    (Springer, 2019-01-18) Boucenna, Fateh; Nouali, Omar; Kechid, Samir; Kechadi, M. Tahar
    Cloud computing is a technology that provides users with a large storage space and an enormous computing power. However, the outsourced data are often sensitive and confidential, and hence must be encrypted before being outsourced. Consequently, classical search approaches have become obsolete and new approaches that are compatible with encrypted data have become a necessity. For privacy reasons, most of these approaches are based on the vector model which is a time consuming process since the entire index must be loaded and exploited during the search process given that the query vector must be compared with each document vector. To solve this problem, we propose a new method for constructing a secure inverted index using two key techniques, homomorphic encryption and the dummy documents technique. However, 1) homomorphic encryption generates very large ciphertexts which are thousands of times larger than their corresponding plaintexts, and 2) the dummy documents technique that enhances the index security produces lots of false positives in the search results. The proposed approach exploits the advantages of these two techniques by proposing two methods called the compressed table of encrypted scores and the double score formula. Moreover, we exploit a second secure inverted index in order to manage the users’ access rights to the data. Finally, in order to validate our approach, we performed an experimental study using a data collection of one million documents. The experiments show that our approach is many times faster than any other approach based on the vector model.