A General Survey of Privacy-Preserving Data Mining Models and Algorithms

TLDR: This paper provides a review of the state-of-the-art methods for privacy, including methods for randomization, k-anonymization, and distributed privacy-preserving data mining, and the computational and theoretical limits associated with privacy- Preserving over high dimensional data sets.

Abstract: In recent years, privacy-preserving data mining has been studied extensively, because of the wide proliferation of sensitive information on the internet. A number of algorithmic techniques have been designed for privacy-preserving data mining. In this paper, we provide a review of the state-of-the-art methods for privacy. We discuss methods for randomization, k-anonymization, and distributed privacy-preserving data mining. We also discuss cases in which the output of data mining applications needs to be sanitized for privacy-preservation purposes. We discuss the computational and theoretical limits associated with privacy-preservation over high dimensional data sets.