Recent Advances in Preserving Privacy Data Mining

Autor: Jing Zhao Li, Gao Ming Yang, Shun Xiang Zhang
Rok vydání: 2013
Předmět:
Zdroj: Advanced Materials Research. :541-544
ISSN: 1662-8985
Popis: A number of privacy preserving techniques have been proposed recently in data mining. In this paper, we provide a review of the state-of-the-art methods for privacy preserving data mining. and discuss methods for randomization, secure multipart computation, and so on. We also make a classification for the privacy preserving data mining technologies, and analyze some works in this field, such as data distortion method for achieving privacy preserving association rule mining. Detailed evaluation criteria of privacy preserving algorithm were illustrated, which include algorithm performance, data utility, and privacy protection degree. Finally, the development of privacy preserving data mining for further directions is given.
Databáze: OpenAIRE