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
[3] C. Blake and C. Merz. UCI repository of machine learning databases, 1998.
[4] Wikipedia Authors, Visual Basic for Applications, Wikipedia, Retrieved on 6 April 2010.
http://en.wikipedia.org/wiki/Visual_Basic_for_Applications
[5] Wolfram Research Inc, Box-Muller Transformation, Wolfram MathWorld, Retrieved on 6 April 2010.
http://mathworld.wolfram.com/Box-MullerTransformation.html
[6] R. Bayardo and R. Agrawal. Data privacy through optimal k-anonymization. In ICDE, 2005.
[7] L. Sweeney. K-Anonimity: A model for protecting privacy, International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, 10(5):557-570, 2002
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