Abstract
A significant amount of application data is of a personal nature. These kind of data sets may contain sensitive information about an individual, such as his or her financial status, political beliefs, sexual orientation, and medical history. The knowledge about such personal information can compromise the privacy of individuals. Therefore, it is crucial to design data collection, dissemination, and mining techniques, so that individuals are assured of their privacy.
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This border is for illustration purposes only, and does not correspond to any data set in this chapter.
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Splitting a uniform distribution into two equal parts reduces its variance by a factor of 4.
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© 2015 Springer International Publishing Switzerland
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Aggarwal, C. (2015). Privacy-Preserving Data Mining. In: Data Mining. Springer, Cham. https://doi.org/10.1007/978-3-319-14142-8_20
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DOI: https://doi.org/10.1007/978-3-319-14142-8_20
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Online ISBN: 978-3-319-14142-8
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