Open AccessOptimized Deep Learning for Enhanced Trade-off in Differentially Private Learning
Author(s) -
P. Geetha,
Chandrakant Naikodi,
L. Padma Suresh
Publication year - 2021
Publication title -
engineering, technology and applied science research/engineering, technology and applied science research
Language(s) - English
Resource type - Journals
eISSN - 2241-4487
pISSN - 1792-8036
DOI - 10.48084/etasr.4017
Subject(s) - big data , analytics , data science , computer science , deep learning , balance (ability) , data analysis , information privacy , artificial intelligence , computer security , data mining , medicine , physical medicine and rehabilitation
Privacy and data analytics are two conflicting domains that have gained interest due to the advancements of technology in the big data era. Organizations in sectors such as finance, healthcare, and e-commerce take advantage of the data collected, to help them enable innovative decision making and analysis. What is sidelined is the fact that the collected data have associated private data of the individuals involved, and may be exploited and used for unjustified purposes. Defending privacy and performing useful analytics are two sides of the same coin, and hence achieving a good balance between these is a challenging scenario. This paper proposes an optimized differentially private deep learning mechanism that enhances the trade-off between the conflicting objectives of privacy, accuracy, and performance. The goal of this paper is to provide an optimal solution that gives a quantifiable trade-off between these contradictory objectives.