Graphical-model based estimation and inference for differential privacy
About
Many privacy mechanisms reveal high-level information about a data distribution through noisy measurements. It is common to use this information to estimate the answers to new queries. In this work, we provide an approach to solve this estimation problem efficiently using graphical models, which is particularly effective when the distribution is high-dimensional but the measurements are over low-dimensional marginals. We show that our approach is far more efficient than existing estimation techniques from the privacy literature and that it can improve the accuracy and scalability of many state-of-the-art mechanisms.
Ryan McKenna, Daniel Sheldon, Gerome Miklau• 2019
Related benchmarks
| Task | Dataset | Result | Rank | |
|---|---|---|---|---|
| Offline Reinforcement Learning | Kitchen Partial | Normalized Score3.5 | 62 | |
| Offline Reinforcement Learning | Maze2D medium | Normalized Return46.8 | 38 | |
| Offline Reinforcement Learning | Maze2D umaze | Normalized Return41.6 | 38 | |
| Offline Reinforcement Learning | Maze2D large | Normalized Return21.9 | 33 | |
| Offline Reinforcement Learning | MuJoCo HalfCheetah | Normalized Return4.5 | 33 | |
| Transition Synthesis | Maze2D umaze | Marginal79.3 | 5 | |
| Transition Synthesis | Maze2D medium | Marginal80.1 | 5 | |
| Transition Synthesis | Kitchen Partial | Marginal78.3 | 5 | |
| Transition Synthesis | Maze2D large | Marginal0.803 | 5 | |
| Transition Synthesis | MuJoCo HalfCheetah | Marginal0.776 | 5 |
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