Abstract
Data mining is the extraction of vast interesting patterns or knowledge from huge amount of data. In recent years, with the explosive developments in Internet, data storage and data processing technologies, privacy preservation has been one of the greater concerns in data mining. Privacy preserving data mining (PPDM) has become increasingly popular because it allows sharing of privacy sensitive data for analysis purposes. Privacy-preserving data mining considers the problem of running data mining algorithms on confidential data that is not supposed to be revealed even to the party running the algorithm. There are two classic settings for privacy-preserving data mining (although these are by no means the only ones). In the First setting, the data is divided among two or more different parties; the aim is to run a data mining algorithm on the union of parties' databases without allowing any party to view another individual's private data. In the second setting, some statistical data that is to be released (so that it can be used for research using statistics and/or data mining) may contain confidential data; hence, it is first modified so that (a) the data does not compromise anyone's privacy, and (b) it is still possible to obtain meaningful results by running data mining algorithms on the modified data set. In recent years, PPDM has been studied extensively, because of the wide proliferation of sensitive information on the internet. This paper provides a wide survey of different PPDM algorithms and analyses of the representative techniques for PPDM, and points out their merits and demerits. Finally the present problems and directions for future research are discussed.
Recommended Citation
Hussein, Abou el ela Abdou; Hamza, Nermin; Shahen, Ashraf A.; and Hefny, Hesham A.
(2012),
A SURVEY OF PRIVACY PRESERVING DATA MINING ALGORITHMS,
Yanbu Journal of Engineering and Science: Vol. 5:
Iss.
1, 10-18.
DOI: https://doi.org/10.53370/001c.24055
Available at:
https://yjes.researchcommons.org/yjes/vol5/iss1/2
