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Mechanisms for Robust Local Differential Privacy.

Milan Lopuhaä-Zwakenberg1, Jasper Goseling1

  • 1Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, 7522 NB Enschede, The Netherlands.

Entropy (Basel, Switzerland)
|March 28, 2024
PubMed
Summary

We introduce robust local differential privacy (RLDP) to protect sensitive data. Our framework ensures privacy against unknown data distributions, enhancing utility and mitigating risks from estimation errors.

Keywords:
Rényi divergencelocal differential privacyrobust optimization

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Area of Science:

  • Computer Science
  • Data Privacy
  • Information Security

Background:

  • Releasing sensitive data requires robust privacy mechanisms.
  • Standard differential privacy can lead to utility loss due to worst-case assumptions.
  • Privacy leaks can occur from discrepancies between estimated and true data distributions.

Purpose of the Study:

  • Introduce a robust local differential privacy (RLDP) framework.
  • Enhance privacy guarantees by considering unknown true data distributions.
  • Mitigate utility penalties associated with traditional differential privacy.

Main Methods:

  • Construct an uncertainty set using Rényi divergence for robust privacy.
  • Employ robust optimization techniques, approximating the uncertainty set with a polytope.
  • Develop low-complexity algorithms based on existing local differential privacy (LDP) mechanisms for scalability.

Main Results:

  • Demonstrate that RLDP mechanisms provide robust privacy guarantees.
  • Achieve high utility, approaching optimal levels, while maintaining privacy.
  • Numerical experiments validate the effectiveness of the proposed mechanisms.

Conclusions:

  • The RLDP framework offers a superior balance between privacy and utility.
  • Robustness against distribution variations is key to preventing privacy leaks.
  • The developed low-complexity algorithms make RLDP practical for large datasets.