Learning With Differential Privacy
This addresses data leakage risks for individuals and organizations, but it is incremental as it reviews existing methods rather than introducing new ones.
The paper discusses differential privacy as a method to protect sensitive data by using randomized responses during data collection, ensuring strong privacy while maintaining utility. It explores its applications, current research approaches, and real-world trade-offs.
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.