Privacy and Fairness Analysis in the Post-Processed Differential Privacy Framework

The post-processed Differential Privacy (DP) framework has been routinely adopted to preserve privacy while maintaining important invariant characteristics of datasets in data-release applications such as census data. Typical invariant characteristics include non-negative counts and total population. Subspace DP has been proposed to preserve total population while guaranteeing DP for sub-populations. Non-negativity post-processing has been identified to inherently incur fairness issues. In this work, we study privacy and unfairness ( i.e ., accuracy disparity) concerns in the post-processed DP framework. On one hand, we propose the post-processed DP framework with both non-negativity and accurate total…

• NS
  National Science and Technology Major Project

  Award: 2022ZD0116800
• YS
  Young Scientists Fund

  Award: 2023HWYQ-113