Privacy-Preserving Data Mining (PPDM)

Ron S. Hirschprung ()
Additional contact information
Ron S. Hirschprung: Ariel University

A chapter in Machine Learning for Data Science Handbook, 2023, pp 887-911 from Springer

Abstract: 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.

Date: 2023
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-24628-9_38

Ordering information: This item can be ordered from
http://www.springer.com/9783031246289

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

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().