No Free Lunch in Data Privacy
Summary: No free lunch: a no-free-lunch theorem shows DP privacy-utility requires data-generation assumptions. Privacy hinges on limiting inference about an individual's participation, not mere tuple presence; DP can breach participation in social networks and tabular data without modeling assumptions. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Kifer (Pennsylvania State University)
- 2. Ashwin Machanavajjhala (Yahoo)
BibTeX Citation
@inproceedings{kifer_sigmod11,
title = {{No Free Lunch in Data Privacy}},
author = {Kifer, Daniel and Machanavajjhala, Ashwin},
series = {{SIGMOD} '11},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1989323.1989345},
url = {https://dl.acm.org/doi/10.1145/1989323.1989345},
year = {2011}
}Incoming Citations (Sorted by Pagerank)
Showing 26 of 26 citing papers.
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Outgoing Citations (Sorted by Pagerank)
Showing 4 of 4 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 122 | Revealing Information while Preserving Privacy | 2003 | PODS | 0.00030770793 |
| 520 | Practical Privacy: The SuLQ Framework | 2005 | PODS | 0.00016939312 |
| 879 | Relationship Privacy: Output Perturbation for Queries with Joins | 2009 | PODS | 0.00013299637 |
| 4,418 | Towards an Axiomatization of Statistical Privacy and Utility | 2010 | PODS | 6.6074165e-05 |
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