Private Synthetic Data Generation in Bounded Memory
Summary: PrivHP provides epsilon-DP synthetic data for streams via a bounded-memory private hierarchical decomposition approximating the input CDF. It uses a pruning parameter k and tail_k to trade space for utility, with private sketches achieving M = O(k log^2|X|) and Wasserstein bounds. (summarized by gpt-5-nano on Feb 09 2026)
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Authors
- 1. Rayne Holland
- 2. Seyit Camtepe
- 3. Chandra Thapa
- 4. Minhui Xue
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Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 111 | Privacy, Accuracy, and Consistency Too: A Holistic Solution to Contingency Table Release | 2007 | PODS | 0.00047073785 |
| 178 | Boosting the Accuracy of Differentially Private Histograms Through Consistency | 2010 | VLDB | 0.00037697111 |
| 719 | Understanding Hierarchical Methods for Differentially Private Histograms | 2013 | VLDB | 0.00017626484 |
| 1,094 | Tight Bounds for Lp Samplers, Finding Duplicates in Streams, and Related Problems | 2011 | PODS | 0.00014129658 |
| 1,520 | PrivTree: A Differentially Private Algorithm for Hierarchical Decompositions | 2016 | SIGMOD | 0.00011535148 |
| 5,131 | Better Differentially Private Approximate Histograms and Heavy Hitters using the Misra-Gries Sketch | 2023 | PODS | 5.6751843e-05 |
| 8,522 | Differentially Private Hierarchical Heavy Hitters | 2024 | PODS | 4.4937074e-05 |
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