Simulation-Based Inference for Differential Privacy — NSF Award to University of Pittsburgh (PA, $18,245)
This research project will deliver tools to obtain accurate and broad statistical conclusions from data that are subject to privacy constraints. Differential Privacy is an increasingly adopted technique to protect data within government and industry, such as in the US 2020 Decennial Census. However, privacy protection
| Award title | Simulation-Based Inference for Differential Privacy |
|---|---|
| Award ID | 2610910 |
| Awardee | University of Pittsburgh |
| City | PITTSBURGH |
| State | PA |
| Amount obligated | $18,245 |
| Principal investigator | Jordan Awan |
| Program | Methodology, Measuremt & Stats |
| Start date | 10/01/2025 |
| Abstract | This research project will deliver tools to obtain accurate and broad statistical conclusions from data that are subject to privacy constraints. Differential Privacy is an increasingly adopted technique to protect data within government and industry, such as in the US 2020 Decennial Census. However, privacy protection comes at a cost in terms of accuracy of the analysis run on these data, sometimes drastically affecting the decisions and conclusions that entail. While employing and training grad |
| Source | NSF Awards |
$799/mo
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