Pattern-Preserving k-Anonymization of sequences and its Application to Mobility Data Mining

Jan 27, 2013

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Outline. MotivationsAnalysis of Sequence Database and Privacy issueOur FrameworkThe problemThe Anonymization Algorithm Experiments Conclusions and Future Work. Motivations (1). Availability of transaction data:Web logsGPS dataClinical dataMany KDD techniques to extract knowledge ab

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Pattern-Preserving k-Anonymization of sequences and its Application to Mobility Data Mining

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Privacy-Preserving Data Mining

Privacy-Preserving Data Mining. Rakesh Agrawal Ramakrishnan Srikant IBM Almaden Research Center 650 Harry Road, San Jose, CA 95120 Published in: ACM SIGMOD International Conference on Management of Data, 2000. Slides by: Adam Kaplan (for CS259, Fall’03). What is Data Mining?.

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Privacy-preserving Anonymization of Set Value Data. Manolis Terrovitis Institute for the Management of Information Systems (IMIS), RC Athena Nikos Mamoulis University of Hong Kong (HKU) Panos Kalnis King Abdullah University of Science and Technology (KAUST). Motivation. Helen.

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Privacy-preserving Anonymization of Set Value Data. Manolis Terrovitis, Nikos Mamoulis University of Hong Kong Panos Kalnis National University of Singapore www.comp.nus.edu.sg/~kalnis. Motivation. Helen. Attacker can see up to m items Any m items

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Data Anonymization – Introduction and k-anonymity. Li Xiong CS573 Data Privacy and Security. Outline. Problem definition K-Anonymity Property Disclosure Control Methods Complexity Attacks against k-Anonymity. Defining Privacy in DB Publishing.

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Privacy Preserving Data Mining

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Data Mining and Its Applications. Data Mining Techniques – For Marketing, Sales, and Customer Support , by Michael J.A. Berry and Gordon Linoff, John Wiley & Sons, Inc., 1997.

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Data Transformation for Privacy-Preserving Data Mining

Database Laboratory. Data Transformation for Privacy-Preserving Data Mining. Stanley R. M. Oliveira Database Systems Laboratory Computing Science Department University of Alberta, Canada. Graduate Seminar November 26 th , 2004. Introduction. Motivation.

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Privacy Preserving Data Mining

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