Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy

Understand

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.