Purifying Approximate Differential Privacy with Randomized Post-processing

• URL: http://arxiv.org/abs/2503.21071v1
• Date: Thu, 27 Mar 2025 01:10:40 GMT
• Title: Purifying Approximate Differential Privacy with Randomized Post-processing
• Authors: Yingyu Lin, Erchi Wang, Yi-An Ma, Yu-Xiang Wang,
• Abstract summary: We propose a framework to convert $(varepsilon, delta)$-approximate Differential Privacy (DP) mechanisms into $(varepsilon, 0)$-pure DP mechanisms.
• Score: 17.069115079029206
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We propose a framework to convert $(\varepsilon, \delta)$-approximate Differential Privacy (DP) mechanisms into $(\varepsilon, 0)$-pure DP mechanisms, a process we call ``purification''. This algorithmic technique leverages randomized post-processing with calibrated noise to eliminate the $\delta$ parameter while preserving utility. By combining the tighter utility bounds and computational efficiency of approximate DP mechanisms with the stronger guarantees of pure DP, our approach achieves the best of both worlds. We illustrate the applicability of this framework in various settings, including Differentially Private Empirical Risk Minimization (DP-ERM), data-dependent DP mechanisms such as Propose-Test-Release (PTR), and query release tasks. To the best of our knowledge, this is the first work to provide a systematic method for transforming approximate DP into pure DP while maintaining competitive accuracy and computational efficiency.

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