Secret Specification Based Personalized Privacy-Preserving Analysis in Big Data

Abstract

The pursuit of refined data analysis and the preservation of privacy in Big Data pose significant concerns. Among the paramount paradigms for addressing these challenges, differential privacy stands out as a vital area of research. However, traditional differential privacy tends to be excessively restrictive when it comes to individuals’ control over their own data. It often treats all data as inherently sensitive, whereas in reality, not all information related to individuals is sensitive and requires an identical level of protection. In this paper, we define secret specification-based differential privacy (SSDP), where the term “secret specification” implies enabling users to decide what aspects of their information are sensitive and what are not, prior to data generation or processing. By allowing individuals to independently define their secret specifications, the SSDP achieves personalized privacy protection and facilitates effective data analysis. To enable the targeted application of SSDP, we further present task-specific mechanisms designed for database and graph data scenarios. Finally, we assess the trade-offs between privacy and utility inherent in the proposed mechanisms through comparative experiments conducted on real datasets, demonstrating the utility enhancements offered by SSDP mechanisms in practical applications.