Generalizing data to provide anonymity when disclosing information (abstract)
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DEXA'07 Proceedings of the 18th international conference on Database and Expert Systems Applications
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Proceedings of the 21st ACM international conference on Information and knowledge management
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Transactions on Data Privacy
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Transactions on Data Privacy
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International Journal of Data Warehousing and Mining
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International Journal of Information Security and Privacy
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Knowledge-Based Systems
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International Journal of Web Services Research
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AusDM '08 Proceedings of the 7th Australasian Data Mining Conference - Volume 87
AIM: a new privacy preservation algorithm for incomplete microdata based on anatomy
ICPCA/SWS'12 Proceedings of the 2012 international conference on Pervasive Computing and the Networked World
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Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Proceedings of the ACM SIGMOD Workshop on Databases and Social Networks
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Proceedings of the 6th ACM India Computing Convention
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WAIM'13 Proceedings of the 14th international conference on Web-Age Information Management
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IEEE/ACM Transactions on Networking (TON)
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ACM Transactions on the Web (TWEB)
A new tool for sharing and querying of clinical documents modeled using HL7 Version 3 standard
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Solving inverse frequent itemset mining with infrequency constraints via large-scale linear programs
ACM Transactions on Knowledge Discovery from Data (TKDD)
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Information Sciences: an International Journal
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Computer Standards & Interfaces
Journal of Computer Security
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Data de-identification reconciles the demand for release of data for research purposes and the demand for privacy from individuals. This paper proposes and evaluates an optimization algorithm for the powerful de-identification procedure known as k-anonymization. A k-anonymized dataset has the property that each record is indistinguishable from at least k - 1 others. Even simple restrictions of optimized k-anonymity are NP-hard, leading to significant computational challenges. We present a new approach to exploring the space of possible anonymizations that tames the combinatorics of the problem, and develop data-management strategies to reduce reliance on expensive operations such as sorting. Through experiments on real census data, we show the resulting algorithm can find optimalk-anonymizations under two representative cost measures and a wide range of k. We also show that the algorithm can produce good anonymizations in circumstances where the input data or input parameters preclude finding an optimal solution in reasonable time. Finally, we use the algorithm to explore the effects of different coding approaches and problem variations on anonymization quality and performance. To our knowledge, this is the first result demonstrating optimal k-anonymization of a nontrivial dataset under a general model of the problem.