Adapting Differential Privacy in Scenarios

What is it about?

Differential Privacy has been widely accepted as a de facto standard within both academia and industry, when it comes to use personal data in a privacy-respecting and privacy-preserving manner. However, according to the philosophy that ``one size does not fit all'', the generalized DP framework is not sufficiently effective to accurately define privacy risks and maximize utility gains in all real-world scenarios. We summarize DP adaptations in real-world scenarios by categorizing them into three types of adaptation separately in two parallel lines of contexts including statistical database privacy and local data privacy.

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Why is it important?

It will demonstrate how Differential Privacy definition and mechanisms can be modified by capturing and modelling featured characteristics of data/database along with incorporating them to update privacy definition.