Essay About K-Anonymity Model And Social Security Number

Achieving Anonymity Via Clustering
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Achieving Anonymity via Clustering
Gagan Aggarwal1
Google Inc.
Mountain View, CA 94043
[email protected]
Tomaґs Feder2
Comp. Sc. Dept.
Stanford University
Stanford, CA 94305
[email protected]
Krishnaram Kenthapadi2
Comp. Sc. Dept.
Stanford University
Stanford, CA 94305
[email protected]
Samir Khuller3
Comp. Sc. Dept.
University of Maryland
College Park, MD 20742
[email protected]
Rina Panigrahy2,4
Comp. Sc. Dept.
Stanford University
Stanford, CA 94305
[email protected]
Dilys Thomas2
Comp. Sc. Dept.
Stanford University
Stanford, CA 94305
[email protected]
An Zhu1
Google Inc.
Mountain View, CA 94043
[email protected]
ABSTRACT
Publishing data for analysis from a table containing personal
records, while maintaining individual privacy, is a problem
of increasing importance today. The traditional approach of
de-identifying records is to remove identifying fields such as
social security number, name etc. However, recent research
has shown that a large fraction of the US population can be
identified using non-key attributes (called quasi-identifiers)
such as date of birth, gender, and zip code [15]. Sweeney [16]
proposed the k-anonymity model for privacy where non-key
attributes that leak information are suppressed or generalized
so that, for every record in the modified table, there are
at least k−1 other records