Enhanced Privacy Bound for Shuffle Model with Personalized Privacy.

Area of Science:

• Computer Science
• Information Security
• Cryptography

Background:

• The shuffle model enhances differential privacy (DP) by anonymizing and shuffling local data.
• Deriving tight privacy bounds in shuffle models is challenging, especially in personalized privacy settings.
• Existing methods often inaccurately capture clone-generating probabilities or underestimate indistinguishability.

Purpose of the Study:

• To develop a more precise analysis for bounding privacy in the shuffle model.
• To address the practical challenge of personalized privacy settings.
• To provide a general and tighter privacy bound for arbitrary DP mechanisms.

Main Methods:

• Derived clone-generating probability using hypothesis testing for accurate characterization.
• Analyzed indistinguishability within the context of epsilon-DP, leveraging distribution convexity.
• Developed a novel analysis for the shuffle model of differential privacy.

Main Results:

• Achieved a more accurate characterization of clone-generating probability.
• Obtained a tighter privacy bound by leveraging distribution convexity in epsilon-DP.
• Demonstrated superior performance of the proposed bound over existing literature through theoretical and numerical results.

Conclusions:

• The developed analysis provides a general and tighter privacy bound for the shuffle model.
• The findings offer significant improvements for privacy-preserving data analysis in personalized settings.
• The study advances the understanding and application of differential privacy in complex scenarios.