Practical Privacy: The SuLQ Framework
Summary: Defines SuLQ: slightly noisy answers to a sublinear number of queries, extending privacy analysis to real-valued queries and arbitrary rows with tighter noise bounds. Shows SuLQ can implement PCA, k‑means, Perceptron, ID3 and most statistical‑query algorithms with few calls. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Avrim Blum (Carnegie Mellon University)
- 2. Cynthia Dwork (Microsoft Research (Silicon Valley))
- 3. Frank McSherry (Microsoft Research (Silicon Valley))
- 4. Kobbi Nissim (Ben Gurion University)
BibTeX Citation
@inproceedings{blum_pods05,
address = {New York, NY, USA},
series = {{PODS} '05},
title = {{Practical Privacy: The SuLQ Framework}},
url = {https://dl.acm.org/doi/10.1145/1065167.1065184},
doi = {10.1145/1065167.1065184},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Blum, Avrim and Dwork, Cynthia and McSherry, Frank and Nissim, Kobbi},
year = {2005}
}
Incoming Citations (Sorted by Pagerank)
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Outgoing Citations (Sorted by Pagerank)
Showing 5 of 5 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 70 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.0003804755 |
| 123 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00031082693 |
| 218 | Limiting Privacy Breaches in Privacy Preserving Data Mining | 2003 | PODS | 0.00024420564 |
| 244 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023476901 |
| 4,738 | Randomizing, a Practical Method for Protecting Statistical Databases Against Compromise | 1982 | VLDB | 6.5286784e-05 |
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