Essay About Cluster Centers 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]
TomaÒs 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 having exactly the same values for
quasi-identifiers. We propose a new method for anonymizing
data records, where quasi-identifiers of data records are
first clustered and then cluster centers are published. To
ensure privacy of the data records, we impose the constraint
1This work was done when the authors were Computer Science
PhD students at Stanford University.
2Supported in part by NSF Grant ITR-0331640. This
work was also supported in part by TRUST (The Team
for Research in Ubiquitous Secure Technology), which receives
support from the National Science Foundation (NSF
award number CCF-0424422) and the following organizations:
Cisco, ESCHER, HP, IBM, Intel, Microsoft, ORNL,
Qualcomm, Pirelli, Sun and Symantec.
3Supported by NSF Award CCF-0430650.
4Supported in part by Stanford Graduate Fellowship.
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