Privacy-preserving Anonymization of Set-valued Data
Summary: Introduces k^m-anonymity for set-valued transactions, treating every item as potentially identifying or sensitive under partial-set knowledge. Uses generalization rather than suppression, with an optimal algorithm and scalable greedy heuristics for near-optimal anonymization. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Manolis Terrovitis (University of Hong Kong)
- 2. Nikos Mamoulis (University of Hong Kong)
- 3. Panos Kalnis (National University of Singapore)
BibTeX Citation
@article{terrovitis_vldb08,
title = {{Privacy-preserving Anonymization of Set-valued Data}},
author = {Terrovitis, Manolis and Mamoulis, Nikos and Kalnis, Panos},
journal = {PVLDB},
series = {{VLDB} '08},
pages = {115},
doi = {10.14778/1453856.1453874},
url = {https://doi.org/10.14778/1453856.1453874},
year = {2008}
}
Incoming Citations (Sorted by Pagerank)
Showing 8 of 8 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 1,453 | PrivBasis: Frequent Itemset Mining with Differential Privacy | 2012 | VLDB | 0.00010597751 |
| 2,417 | On Differentially Private Frequent Itemset Mining | 2013 | VLDB | 8.4922039e-05 |
| 2,913 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB | 7.8649948e-05 |
| 4,432 | Anonymization of Set-Valued Data via Top-Down, Local Generalization | 2009 | VLDB | 6.5992973e-05 |
| 5,621 | rho-uncertainty: Inference-Proof Transaction Anonymization | 2010 | VLDB | 6.0588309e-05 |
| 8,880 | Set-valued Data Publication with Local Privacy: Tight Error Bounds and Efficient Mechanisms | 2020 | VLDB | 5.2572559e-05 |
| 9,270 | Privacy Preservation by Disassociation | 2012 | VLDB | 5.2032182e-05 |
| 12,802 | Anonymized Data: Generation, Models, Usage | 2009 | SIGMOD | 4.9769913e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 7 of 7 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 164 | Mining Frequent Patterns without Candidate Generation | 2000 | SIGMOD | 0.000273994 |
| 404 | On the Complexity of Optimal K-Anonymity | 2004 | PODS | 0.00019072866 |
| 470 | Incognito: Efficient Full-Domain K-Anonymity | 2005 | SIGMOD | 0.00017747844 |
| 584 | Anatomy: Simple and Effective Privacy Preservation | 2006 | VLDB | 0.00015947172 |
| 3,166 | Achieving Anonymity via Clustering | 2006 | PODS | 7.5732039e-05 |
| 4,805 | Fast Data Anonymization with Low Information Loss | 2007 | VLDB | 6.406552e-05 |
| 6,682 | Approximate Algorithms for k-Anonymity | 2007 | SIGMOD | 5.7079897e-05 |
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Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 8,048 | Privacy-Enhancing k-Anonymization of Customer Data | 2005 | PODS |
| 2 | 1,323 | Minimality Attack in Privacy Preserving Data Publishing | 2007 | VLDB |
| 3 | 3,166 | Achieving Anonymity via Clustering | 2006 | PODS |
| 4 | 12,721 | Non-homogeneous Generalization in Privacy Preserving Data Publishing | 2010 | SIGMOD |
| 5 | 347 | Generalizing Data to Provide Anonymity when Disclosing Information | 1998 | PODS |
| 6 | 4,432 | Anonymization of Set-Valued Data via Top-Down, Local Generalization | 2009 | VLDB |
| 7 | 2,913 | Publishing Set-Valued Data via Differential Privacy | 2011 | VLDB |
| 8 | 9,270 | Privacy Preservation by Disassociation | 2012 | VLDB |
| 9 | 2,453 | Personalized Privacy Preservation | 2006 | SIGMOD |
| 10 | 4,805 | Fast Data Anonymization with Low Information Loss | 2007 | VLDB |