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Learning With Differential Privacy
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Author(s): Poushali Sengupta (University of Kalyani, India), Sudipta Paul (National Institute of Science, Education, and Research, Bhubaneswar, India & Homi Bhaba National Institute, Mumbai, India)and Subhankar Mishra (National Institute of Science, Education, and Research, Bhubaneswar, India & Homi Bhaba National Institute, Mumbai, India)
Copyright: 2021
Pages: 24
Source title:
Handbook of Research on Cyber Crime and Information Privacy
Source Author(s)/Editor(s): Maria Manuela Cruz-Cunha (Polytechnic Institute of Cávado and Ave, Portugal)and Nuno Mateus-Coelho (Lusófona University, Portugal)
DOI: 10.4018/978-1-7998-5728-0.ch019
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Abstract
The leakage of data might have an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection systems are prone to leakage. Differential privacy comes to the rescue with a proper promise of protection against leakage, as it uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility. Differential privacy allows one to access the forest of data by describing their pattern of groups without disclosing any individual trees. The current adaption of differential privacy by leading tech companies and academia encourages authors to explore the topic in detail. The different aspects of differential privacy, its application in privacy protection and leakage of information, a comparative discussion on the current research approaches in this field, its utility in the real world as well as the trade-offs will be discussed.
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