Privacy Preserving of Shared Data in Deep Learning

Abstract

In the age of Big Data the need of developing machine learning algorithms has increased. Such algorithms are used to extract valuable information needed for different types of sectors; health, education, financial …etc. In many cases data has to be shared among several parties to guarantee better accuracy of the results of such algorithms. In these cases privacy of data would be questionable. In this paper a survey has been conducted on research that focused on Privacy Preserving techniques when applying deep learning algorithms on distributed or shared data.