Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
This work addresses the trade-off between privacy and utility in data protection for users and applications relying on sensitive data, representing an incremental improvement over existing LDP methods.
The paper tackles the problem of uniform privacy protection degrading downstream task performance in Local Differential Privacy (LDP) by proposing Bayesian Coordinate Differential Privacy (BCDP), which enables feature-specific privacy quantification and improves accuracy in tasks like mean estimation and regression without compromising privacy.
Local Differential Privacy (LDP) offers strong privacy guarantees without requiring users to trust external parties. However, LDP applies uniform protection to all data features, including less sensitive ones, which degrades performance of downstream tasks. To overcome this limitation, we propose a Bayesian framework, Bayesian Coordinate Differential Privacy (BCDP), that enables feature-specific privacy quantification. This more nuanced approach complements LDP by adjusting privacy protection according to the sensitivity of each feature, enabling improved performance of downstream tasks without compromising privacy. We characterize the properties of BCDP and articulate its connections with standard non-Bayesian privacy frameworks. We further apply our BCDP framework to the problems of private mean estimation and ordinary least-squares regression. The BCDP-based approach obtains improved accuracy compared to a purely LDP-based approach, without compromising on privacy.