Handicapping attacker's confidence: an alternative to <Emphasis Type="Italic">k</Emphasis>-anonymization |
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Authors: | Ke Wang Benjamin C M Fung Philip S Yu |
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Affiliation: | (1) School of Computer Science, Simon Fraser University, Simon, BC, Canada, V5A 1S6;(2) IBM T. J. Watson Research Center, Hawthorne, NY 10532, USA |
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Abstract: | We present an approach of limiting the confidence of inferring sensitive properties to protect against the threats caused
by data mining abilities. The problem has dual goals: preserve the information for a wanted data analysis request and limit
the usefulness of unwanted sensitive inferences that may be derived from the release of data. Sensitive inferences are specified
by a set of “privacy templates". Each template specifies the sensitive property to be protected, the attributes identifying
a group of individuals, and a maximum threshold for the confidence of inferring the sensitive property given the identifying
attributes. We show that suppressing the domain values monotonically decreases the maximum confidence of such sensitive inferences.
Hence, we propose a data transformation that minimally suppresses the domain values in the data to satisfy the set of privacy
templates. The transformed data is free of sensitive inferences even in the presence of data mining algorithms. The prior
k-anonymization k has been italicized consistently throughout this article. focuses on personal identities. This work focuses on the association
between personal identities and sensitive properties.
Ke Wang received Ph.D. from Georgia Institute of Technology. He is currently a professor at School of Computing Science, Simon Fraser
University. Before joining Simon Fraser, he was an associate professor at National University of Singapore. He has taught
in the areas of database and data mining. Dr. Wang’s research interests include database technology, data mining and knowledge
discovery, machine learning, and emerging applications, with recent interests focusing on the end use of data mining. This
includes explicitly modeling the business goal (such as profit mining, bio-mining and web mining) and exploiting user prior
knowledge (such as extracting unexpected patterns and actionable knowledge). He is interested in combining the strengths of
various fields such as database, statistics, machine learning and optimization to provide actionable solutions to real-life
problems. He is an associate editor of the IEEE TKDE journal and has served program committees for international conferences.
Benjamin C. M. Fung received B.Sc. and M.Sc. degrees in computing science from Simon Fraser University. Received the postgraduate scholarship
doctoral award from the Natural Sciences and Engineering Research Council of Canada (NSERC), Mr. Fung is currently a Ph.D.
candidate at Simon Fraser. His recent research interests include privacy-preserving data mining, secure distributed computing,
and text mining. Before pursuing his Ph.D., he worked in the R&D Department at Business Objects and designed reporting systems
for various Enterprise Resource Planning (ERP) and Customer Relationship Management (CRM) systems, including BaaN, Siebel,
and PeopleSoft. Mr. Fung has published in data engineering, data mining, and security conferences, journals, and books, including
IEEE ICDE, IEEE ICDM, IEEE ISI, SDM, KAIS, and the Encyclopedia of Data Warehousing and Mining.
Philip S. Yu received B.S. degree in E.E. from National Taiwan University, M.S. and Ph.D. degrees in E.E. from Stanford University, and
M.B.A. degree from New York University. He is with IBM T.J. Watson Research Center and currently manager of the Software Tools
and Techniques group. Dr. Yu has published more than 450 papers in refereed journals and conferences. He holds or has applied
for more than 250 US patents. Dr. Yu is a Fellow of the ACM and the IEEE. He has received several IBM honors including two
IBM Outstanding Innovation Awards, an Outstanding Technical Achievement Award, two Research Division Awards and the 85th plateau
of Invention Achievement Awards. He received a Research Contributions Award from IEEE International Conference on Data Mining
in 2003 and also an IEEE Region 1 Award for “promoting and perpetuating numerous new electrical engineering concepts” in 1999.
Dr. Yu is an IBM Master Inventor. |
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Keywords: | Privacy protection k-anonymity Sensitive inference Data mining Classification Data sharing |
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