Limiting Privacy Breaches in Privacy Preserving Data Mining
Summary: Formulates record-level privacy breaches and proposes "amplification", a distribution-agnostic method that guarantees bounds on breach risk. Applies it to association-rule mining with modified randomization plus PRG-seed encoding to shrink randomized transactions and introduces breach-aware privacy metrics. (summarized by gpt-5-mini on Feb 09 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Alexandre Evfimievski (Cornell University)
- 2. Johannes Gehrke (Cornell University)
- 3. Ramakrishnan Srikant (IBM)
BibTeX Citation
@inproceedings{evfimievski_pods03,
address = {New York, NY, USA},
series = {{PODS} '03},
title = {{Limiting Privacy Breaches in Privacy Preserving Data Mining}},
url = {https://dl.acm.org/doi/10.1145/773153.773174},
doi = {10.1145/773153.773174},
booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
publisher = {Association for Computing Machinery},
author = {Evfimievski, Alexandre and Gehrke, Johannes and Srikant, Ramakrishnan},
year = {2003}
}
Incoming Citations (Sorted by Pagerank)
Showing 39 of 39 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 |
|---|---|---|---|---|
| 27 | Fast Algorithms for Mining Association Rules | 1994 | VLDB | 0.00052255472 |
| 70 | Privacy-Preserving Data Mining | 2000 | SIGMOD | 0.0003804755 |
| 244 | On the Design and Quantification of Privacy Preserving Data Mining Algorithms | 2001 | PODS | 0.00023476901 |
| 1,217 | Maintaining Data Privacy in Association Rule Mining | 2002 | VLDB | 0.00011627624 |
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