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Achieving Anonymity via Clustering
Gagan Aggarwal1
Google Inc.
Mountain View, CA 94043
gagan@cs.stanford.edu
Toma´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......
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Title: Achieving Anonymity Via Clustering
Approximate Word Count: 523
Approximate Pages: 3 (250 words per double-spaced page)
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Achieving Anonymity Via Clustering
Achieving Anonymity via Clustering. Achieving Anonymity via Clustering Gagan
Aggarwal1 Google Inc. Mountain View, CA 94043 gagan@cs ... ...
