Scenario-based Adaptations of Differential Privacy: A Technical Survey
Ying Zhao, Jia Tina Du, Jinjun Chen
Abstract
Differential privacy has been a de facto privacy standard in defining privacy and handling privacy preservation. It has had great success in scenarios of local data privacy and statistical dataset privacy. As a primitive definition, standard differential privacy has been adapted to a wide range of practical scenarios. In this work, we summarize differential privacy adaptations in specific scenarios and analyze the correlations between data characteristics and differential privacy design. We mainly present them in two lines including differential privacy adaptations in local data privacy and differential privacy adaptations in statistical dataset privacy. With a focus on differential privacy design, this survey targets providing guiding rules in differential privacy design for scenarios, together with identifying potential opportunities to adaptively apply differential privacy in more emerging technologies and further improve differential privacy itself with the assistance of cryptographic primitives.