Privacy-Related Aspects and Techniques
- Stan MatwinAffiliated withUniversity of Ottawa
The privacy-preserving aspects and techniques of machine learning cover the family of methods and architectures developed to protect the privacy of people whose data are used by machine learning (ML) algorithms. This field, also known as privacy-preserving data mining (PPDM), addresses the issues of data privacy in ML and data mining. Most existing methods and approaches are intended to hide the original data from the learning algorithm, while there is emerging interest in methods ensuring that the learned model does not reveal private information. Another research direction contemplates methods in which several parties bring their data into the model-building process without mutually revealing their own data.
Motivation and Background
The key concept for any discussion of the privacy aspects of data mining is the definition of privacy. After Alan Westin, we understand privacy as the ability “of in ...
- Privacy-Related Aspects and Techniques
- Reference Work Title
- Encyclopedia of Machine Learning
- pp 795-801
- Print ISBN
- Online ISBN
- Springer US
- Copyright Holder
- Springer Science+Business Media, LLC
- Additional Links
- Industry Sectors
- eBook Packages
- Editor Affiliations
- 221. School of Computer Science and Engineering, University of New South Wales
- 222. Faculty of Information Technology, Clayton School of Information Technology, Monash University
- Stan Matwin (1)
- Author Affiliations
- 1. University of Ottawa, Ottawa, ON, Canada
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