Accelerated Search over Encrypted Cloud Data

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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.
Cloud computing, searchable encryption, data privacy, parallel computing.