Encyclopedia of Machine Learning and Data Mining

2017 Edition
| Editors: Claude Sammut, Geoffrey I. Webb

Privacy-Related Aspects and Techniques

Reference work entry
DOI: https://doi.org/10.1007/978-1-4899-7687-1_668



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 individuals to determine for...

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Recommended Reading

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Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.University of OttawaOttawaCanada
  2. 2.Polish Academy of SciencesWarsawPoland