Privacy-aware filter-based feature selection
Abstract
A large amount of digital information collected and stored in databases creates new opportunities for knowledge discovery and data mining. The datasets, however, may contain personally identifiable information that needs to be protected. With high dimensionality of many large datasets, dimensionality reduction such as feature selection becomes indispensible. In this work, we aim at incorporating privacy into the very process of feature selection and as such, propose a privacy-aware filter-based feature selection method (PF-IFR). Our method enables data custodians to define a trade-off measure for controlling the amount of privacy and efficacy using filter-based feature selection techniques.
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Much of the literature assumes a priori knowledge of the set of quasi-identifiers, or seeks to discover them on a case-by-case basis.
“Yet, for correlated or otherwise related variables (often referred to as proxies or quasiidentiiers), much of the literature assumes a priori knowledge of the set of quasi-identiiers [111], or seeks to discover them on a case-by-case basis (e.g.[150,217]) towards the idea of privacy preserving data mining [6].”
From Fairness in machine learning: A survey (2024) · cites this paper as [150]
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Self-citation
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Fairness in machine learning: A survey
Caton, S., & Haas, C. (2024) · ACM Computing Surveys · doi:10.1145/3616865 · Cited by 646 · see citing works
Fairness in Machine Learning: A Survey
Caton, S., & Haas, C. (2020) · arXiv · doi:10.48550/arxiv.2010.04053 · Cited by 646 · see citing works
- Authors
- Jafer, Y., Matwin, S., & Sokolova, M.
- Affiliations
- University of Ottawa (CA)
- Dalhousie University (CA)
- Venue
- 2014 IEEE International Conference on Big Data (Big Data)
- Publisher
- IEEE
- Published
- 2014-10
- Pages
- 1-5
- OpenAlex
- W2027564307
- Cited by
- 14
- References
- 24
- Citations by year
- 2026: 2 · 2023: 2 · 2022: 3 · 2020: 1 · 2017: 2 · 2015: 4
- FWCI
- 1.18
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Big Data (BigData) (publishedVersion)
- Funders
- Natural Sciences and Engineering Research Council of Canada
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Social Sciences › Sociology and Political Science › Privacy, Security, and Data Protection
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- filter-based feature selection, personally identifiable information, privacy-utility trade-off, dimensionality reduction, data privacy, data custodians, privacy-preserving data mining
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