An Efficient and Privacy-preserving Similarity Evaluation For Big Data Analytics

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dc.contributor.authorGheid, Zakaria
dc.contributor.authorChallal, Yacine
dc.date.accessioned2015-09-09T12:55:00Z
dc.date.available2015-09-09T12:55:00Z
dc.date.issued2015-12
dc.description.abstractBig data systems are gathering more and more information in order to discover new values through data analytics and depth insights. However, mining sensitive personal information breaches privacy and degrades services’ reputation. Accordingly, many research works have been proposed to address the privacy issues of data analytics, but almost seem to be not suitable in big data context either in data types they support or in computation time efficiency. In this paper we propose a novel privacy-preserving cosine similarity computation protocol that will support both binary and numerical data types within an efficient computation time, and we prove its adequacy for big data high volume, high variety and high velocity.fr_FR
dc.identifier.urihttp://dl.cerist.dz/handle/CERIST/773
dc.publisherACM/IEEEfr_FR
dc.relation.ispartofseriesUCC;
dc.relation.placeCyprusfr_FR
dc.structureSécurité Informatiquefr_FR
dc.subjectbig data, data analytics, cosine similarity, privacy.fr_FR
dc.titleAn Efficient and Privacy-preserving Similarity Evaluation For Big Data Analyticsfr_FR
dc.typeConference paper
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