Open AccessK-Anonymity Versus L-Diversity: A Comparative Analysis on Data Anonymization Techniques
Author(s) -
C N Sowmyarani,
P Dayananda
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i3.4.14669
Subject(s) - data publishing , computer science , anonymity , data anonymization , adversary , publishing , information privacy , k anonymity , data science , internet privacy , process (computing) , computer security , political science , law , operating system
The main aim of data publishing is to make the data utilized by the researchers, scientists and data analysts to process the data by analytics and statistics which in turn useful for decision making. This data in its original form may contain some person-specific information, which should not be disclosed while publishing the data. So, privacy of such individuals should be preserved. Hence, privacy preserving data publishing plays a major role in providing privacy for person-specific data. The data should be published in such a way that, there should not be any technical way for adversary to infer the information of specific individuals. This paper provides overview on popular privacy preserving techniques. In this study, a honest effort shows that, concepts behind these techniques are analyzed and justified with suitable examples, drawbacks and vulnerability of these techniques towards privacy attacks are narrated.