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Cited by 294

The DOI matches its Crossref record; 124 of the 124 references it cites were found in a registry or an index.

Privacy-Preserving Data Mining: Methods, Metrics, and Applications

Open access
Mendes, Ricardo, Vilela, Joao P.·2017·IEEE Access
DOI 10.1109/access.2017.2706947
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Abstract

The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed.

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Metadata

Authors
Mendes, R., & Vilela, J. P.
Venue
IEEE Access
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
2017
Volume
5
Pages
10562-10582
ISSN
2169-3536 (ISSN-L)
DOI
10.1109/access.2017.2706947
OpenAlex
W2626533970
Cited by
294
References
124
FWCI
17.55
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: Privacy-Preserving Data Mining: Methods, Metrics, and Applications. https://app.cochat.ai/papers/10.1109/access.2017.2706947
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Mendes, R., & Vilela, J. P. (2017). Privacy-Preserving Data Mining: Methods, Metrics, and Applications. IEEE Access. https://doi.org/10.1109/access.2017.2706947

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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.