Privacy-Preserving Data Mining (PPDM)

Ron S. Hirschprung

doi:10.1007/978-3-031-24628-9_38

Privacy-Preserving Data Mining (PPDM)

Ron S. Hirschprung

Department of Industrial Engineering and Management

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Abstract

Data mining is a highly productive tool, while also a source for privacy violation. Privacy has become one of the most significant concerns in the digital era, mainly due to the information disclosure enabled by data mining. Privacy-preserving data mining (PPDM) is a collection of methodologies aimed to minimize and control the amount of private information disclosure in data mining processes. I present the various approaches to achieve PPDM: anonymization, randomization, cryptography, and privatizing results as well as various common methodologies and techniques used to implement these approaches.

Original language	English
Title of host publication	Machine Learning for Data Science Handbook
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Pages	887-911
Number of pages	25
ISBN (Electronic)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_38
State	Published - 1 Jan 2023

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10.1007/978-3-031-24628-9_38

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Privacy-Preserving Data Mining (PPDM)

Abstract

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