Summary of Enhancing Feature-specific Data Protection Via Bayesian Coordinate Differential Privacy, by Maryam Aliakbarpour et al.
Enhancing Feature-Specific Data Protection via Bayesian Coordinate Differential Privacy
by Maryam Aliakbarpour, Syomantak Chaudhuri, Thomas A. Courtade, Alireza Fallah, Michael I. Jordan
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR); Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Bayesian Coordinate Differential Privacy (BCDP), a novel framework that enhances Local Differential Privacy (LDP) by incorporating feature-specific privacy quantification. Unlike traditional LDP approaches, which provide uniform protection to all data features, BCDP adjusts privacy protection according to the sensitivity of each feature, enabling improved performance in downstream tasks without compromising on privacy. The authors demonstrate the properties and connections of BCDP with standard non-Bayesian privacy frameworks, applying it to private mean estimation and ordinary least-squares regression problems. Experimental results show that BCDP-based approaches achieve better accuracy compared to purely LDP-based methods while maintaining strong privacy guarantees. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to keep data private while still using it for important tasks like calculating averages or doing statistical analyses. Right now, there are ways to make data private called Local Differential Privacy (LDP), but they treat all the different parts of the data equally. This can be bad because some parts of the data might not be very sensitive and don’t need as much protection. The authors propose a new method that looks at how important each part of the data is and adjusts the privacy settings accordingly. They show that this new approach works better for certain tasks without sacrificing privacy. |
Keywords
* Artificial intelligence * Regression
