C4.5: programs for machine learning
C4.5: programs for machine learning
Privacy-preserving data mining
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Machine Learning
Privacy Preserving Data Mining
CRYPTO '00 Proceedings of the 20th Annual International Cryptology Conference on Advances in Cryptology
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Building decision tree classifier on private data
CRPIT '14 Proceedings of the IEEE international conference on Privacy, security and data mining - Volume 14
When do data mining results violate privacy?
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Top-Down Specialization for Information and Privacy Preservation
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
On the complexity of optimal K-anonymity
PODS '04 Proceedings of the twenty-third ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Incognito: efficient full-domain K-anonymity
Proceedings of the 2005 ACM SIGMOD international conference on Management of data
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
PKDD'05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases
Privacy-Preserving decision trees over vertically partitioned data
DBSec'05 Proceedings of the 19th annual IFIP WG 11.3 working conference on Data and Applications Security
Privacy-preserving data mining: A feature set partitioning approach
Information Sciences: an International Journal
Optimal constraint-based decision tree induction from itemset lattices
Data Mining and Knowledge Discovery
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In this paper we explore an approach to privacy preserving data mining that relies on the k-anonymity model. The k-anonymity model guarantees that no private information in a table can be linked to a group of less than k individuals. We suggest extended definitions of k-anonymity that allow the k-anonymity of a data mining model to be determined. Using these definitions, we present decision tree induction algorithms that are guaranteed to maintain k-anonymity of the learning examples. Experiments show that embedding anonymization within the decision tree induction process provides better accuracy than anonymizing the data first and inducing the tree later.