Learning With Differential Privacy

• URL: http://arxiv.org/abs/2006.05609v2
• Date: Thu, 11 Jun 2020 14:11:44 GMT
• Title: Learning With Differential Privacy
• Authors: Poushali Sengupta, Sudipta Paul, Subhankar Mishra
• Abstract summary: Differential privacy comes to the rescue with a proper promise of protection against leakage. It uses a randomized response technique at the time of collection of the data which promises strong privacy with better utility.
• Score: 3.618133010429131
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system 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, it's 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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