Attacks on Privacy and deFinetti's Theorem
Summary: Exchangeability and deFinetti's theorem for privacy reasoning. Practical attack on Anatomy and similar sanitizers (random-worlds/iid/tuple-independent); needs only one non-sensitive attribute and a trained model, exposing flaws in prior disclosure probabilities. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Daniel Kifer (Pennsylvania State University)
BibTeX Citation
@inproceedings{kifer_sigmod09,
title = {{Attacks on Privacy and deFinetti's Theorem}},
author = {Kifer, Daniel},
series = {{SIGMOD} '09},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/1559845.1559861},
url = {https://dl.acm.org/doi/10.1145/1559845.1559861},
year = {2009}
}
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