Differential Privacy and the US Census
Summary: Describes the US Census Bureau's adoption of differential privacy for the 2020 decennial, stressing DP's rigorous, composition-aware protection against arbitrary auxiliary information and adversaries. Reports theory-to-practice lessons from nationwide deployment and pinpoints open challenges in accuracy-privacy tradeoffs, algorithm design, and policy. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
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Incoming Citations (Sorted by Pagerank)
Showing 5 of 5 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 4,375 | Sample Debiasing in the Themis Open World Database System | 2020 | SIGMOD | 6.2427076e-05 |
| 8,283 | Measuring Re-identification Risk | 2023 | SIGMOD | 4.5435639e-05 |
| 9,766 | DPXPlain: Privately Explaining Aggregate Query Answers | 2023 | VLDB | 4.2856106e-05 |
| 10,015 | Differentially Private Explanations for Clusters | 2026 | SIGMOD | 4.1945683e-05 |
| 11,143 | DP-PQD: Privately Detecting Per-Query Gaps In Synthetic Data Generated By Black-Box Mechanisms | 2024 | VLDB | 4.1945683e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|
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