My Research Paper - Comparison between 2 models of K-Anonymity: Incognito and Mondrian

TOPIC

Comparison between 2 models of K-Anonymity: Incognito and Mondrian

ABSTRACT

Often organizations publish microdata for purposes such as public health and demographic research. Although attributes that could identify the individuals such as names and IDs are removed, by combining information (such as Zipcode and Birthdays) within different database, these individuals could still be identified through “Joining Attacks” – Combining 2 different database by cross-linking the similar information.

K-Anonymity has been proposed as a mechanism for protecting privacy by generalizing or suppressing certain portion of the released microdata. There had been several models of K-Anonymity since, each release attempts to improve the previous version’s weaknesses. In this paper, we will compare 2 models of K-Anonymity, namely Incognito (full-domain generalization) and Mondrian (multidimensional model), in terms of their effectiveness as well as their performance.

GENERAL TERMS

Algorithms, Experimentation, Theory

KEYWORDS

K-Anonymity, Incognito, Mondrian

REFERENCES

[1] Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan, 2005. Incognito: Efficient FullDomain KAnonymity. University of Wisconsin, Madison. http://www.cse.iitb.ac.in/dbms/Data/Courses/CS632/Papers/incognito.pdf
[2] Kristen LeFevre, David J. DeWitt, Raghu Ramakrishnan, 2006. Mondrian Multidimensional K-Anonymity. University of Wisconsin, Madison.
http://www.cse.iitb.ac.in/dbms/Data/Courses/CS632/Papers/kanon-lefevre-icde06.pdf
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