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Achieving Anonymity Via Clustering

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Achieving Anonymity via Clustering

Gagan Aggarwal1

Google Inc.

Mountain View, CA 94043

gagan@cs.stanford.edu

TomaÒ's Feder2

Comp. Sc. Dept.

Stanford University

Stanford, CA 94305

tomas@cs.stanford.edu

Krishnaram Kenthapadi2

Comp. Sc. Dept.

Stanford University

Stanford, CA 94305

kngk@cs.stanford.edu

Samir Khuller3

Comp. Sc. Dept.

University of Maryland

College Park, MD 20742

samir@cs.umd.edu

Rina Panigrahy2,4

Comp. Sc. Dept.

Stanford University

Stanford, CA 94305

rinap@cs.stanford.edu

Dilys Thomas2

Comp. Sc. Dept.

Stanford University

Stanford, CA 94305

dilys@cs.stanford.edu

An Zhu1

Google Inc.

Mountain View, CA 94043

anzhu@cs.stanford.edu

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.

Permission to make digital or hard copies of all or part of this work for

personal or classroom use is granted without fee provided that copies are

not made or distributed for profit or commercial advantage and that copies

bear this notice and the full

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