Feature Subset Selection in Text-Learning
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Improving Text Classification by Shrinkage in a Hierarchy of Classes
ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Achieving k-anonymity privacy protection using generalization and suppression
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
\ell -Diversity: Privacy Beyond \kappa -Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Detecting privacy leaks using corpus-based association rules
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
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This is a demonstration of a system for protecting sensitive topics present in text documents. Our system works in a privacy framework where the topic is characterized as a multiclass classification problem in a generative setting. We show how our system helps a user redact a document in a business setting to obscure what company the text pertains to, and show some experimental results on redacting the topic for a standard text classification data set.