The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
A General Survey of Privacy-Preserving Data Mining Models and Algorithms
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Citations
Cites
Calibrating Noise to Sensitivity in Private Data Analysis
Dwork, C., McSherry, F., Nissim, K., & Smith, A. (2006) · Lecture Notes in Computer Science · doi:10.1007/11681878_14 · Cited by 7,272
t-Closeness: Privacy Beyond k-Anonymity and l-Diversity
Li, N., Li, T., & Venkatasubramanian, S. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367856 · Cited by 3,451
Our Data, Ourselves: Privacy Via Distributed Noise Generation
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., & Naor, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11761679_29 · Cited by 1,835
Tools for privacy preserving distributed data mining
Clifton, C., Kantarcioglu, M., Vaidya, J., Lin, X., & Zhu, M. Y. (2002) · ACM SIGKDD Explorations Newsletter · doi:10.1145/772862.772867 · Cited by 890
Privacy-Preserving Decision Trees over Vertically Partitioned Data
Vaidya, J., & Clifton, C. (2005) · Lecture Notes in Computer Science · doi:10.1007/11535706_11 · Cited by 144
Cited by
Data Quality in Internet of Things: A state-of-the-art survey
Karkouch, A., Mousannif, H., Al Moatassime, H., & Noel, T. (2016) · Journal of Network and Computer Applications · doi:10.1016/j.jnca.2016.08.002 · Cited by 378
Privacy in the Smart City—Applications, Technologies, Challenges, and Solutions
Eckhoff, D., & Wagner, I. (2018) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2017.2748998 · Cited by 287
Synthetic data generation for tabular health records: A systematic review
Hernandez, M., Epelde, G., Alberdi, A., Cilla, R., & Rankin, D. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.04.053 · Cited by 278
Privacy-preserving collaborative recommendations based on random perturbations
Polatidis, N., Georgiadis, C. K., Pimenidis, E., & Mouratidis, H. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2016.11.018 · Cited by 80
(2019) · Cited by 62
Metadata
- Authors
- Aggarwal, C. C., & Yu, P. S.
- Venue
- Advances in Database Systems
- Publisher
- Springer US
- Published
- 2008
- Pages
- 11-52
- ISSN
- 1386-2944 (ISSN-L)
- OpenAlex
- W184146824
- Cited by
- 469
- References
- 5
- FWCI
- 6.71
- Type
- book chapter
- Language
- en
- Open access
- closed
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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.
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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.
