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dc.contributor.authorAlbishri, Farah
dc.contributor.authorAlnajar, Danya
dc.date.accessioned2021-08-25T11:23:10Z
dc.date.available2021-08-25T11:23:10Z
dc.date.issued2021-08-19
dc.identifier.urihttp://hdl.handle.net/10754/670787
dc.description.abstractWith the acceleration of development in technology, the significance of data andits analysis increases. While The protection of the privacy of data providers is crucial the organizations rely heavily on data analysis using artificial intelligence algorithms to benefit from them economically, politically and socially ...etc. In big data analysis, one of the most important aspects is dimensionality reduction but how we can provide a new concept of privacy in this area? Differential privacy is a mathematical framework that anonymizes data and acts in a privacy-preserving manner with a large scope of future development.
dc.relation.urlhttps://epostersonline.com//ssi2021/node/81
dc.titleDifferential Privacy In Generalized Eigenvalue Problem (Gep)
dc.typePoster
dc.conference.dateAUGUST 19, 2021
dc.conference.nameSaudi Summer Internship Program (SSI) 2021
dc.conference.locationVirtual Event
refterms.dateFOA2021-08-25T11:23:10Z


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