  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.
