The DOI matches its Crossref record.
Using unknowns to prevent discovery of association rules
Open accessAbstract
Data mining technology has given us new capabilities to identify correlations in large data sets. This introduces risks when the data is to be made public, but the correlations are private. We introduce a method for selectively removing individual values from a database to prevent the discovery of a set of rules, while preserving the data for other applications. The efficacy and complexity of this method are discussed. We also present an experiment showing an example of this methodology.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Preserving privacy by de-identifying face images
Newton, E. M., Sweeney, L., & Malin, B. (2005) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2005.32 · Cited by 615
Liu, K., Kargupta, H., & Ryan, J. (2006) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2006.14 · Cited by 554
Maintaining Data Privacy in Association Rule Mining
Rizvi, S. J., & Haritsa, J. R. (2002) · VLDB '02: Proceedings of the 28th International Conference on Very Large Databases · doi:10.1016/b978-155860869-6/50066-4 · Cited by 485
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
Aggarwal, C. C., & Yu, P. S. (2008) · Advances in Database Systems · doi:10.1007/978-0-387-70992-5_2 · Cited by 469
Anonymizing transaction databases for publication
Xu, Y., Wang, K., Fu, A. W.-C., & Yu, P. S. (2008) · Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1401890.1401982 · Cited by 187
Metadata
- Authors
- Saygin, Y., Verykios, V. S., & Clifton, C.
- Venue
- ACM SIGMOD Record
- Published
- 2001-12
- Volume
- 30
- Issue
- 4
- Pages
- 45-54
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W1977493358
- Cited by
- 338
- References
- 0
- FWCI
- 9.39
- Type
- journal article
- Language
- en
- Open access
- green
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Using unknowns to prevent discovery of association rules. https://app.cochat.ai/papers/10.1145/604264.604271
- The paper
- Saygin, Y., Verykios, V. S., & Clifton, C. (2001). Using unknowns to prevent discovery of association rules. ACM SIGMOD Record. https://doi.org/10.1145/604264.604271
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
