Differential Privacy In Generalized Eigenvalue Problem (Gep)

Abstract

With 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.