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cs.HC2024
Maximizing Information Gain in Privacy-Aware Active Learning of Email Anomalies
Mu-Huan Miles Chung, Sharon Li, Jaturong Kongmanee +7
Redacted emails satisfy most privacy requirements but they make it more difficult to detect anomalous emails that may be indicative of data exfiltration. In this paper we develop a…
cs.CL2024★ 1 cited
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active Learning
Wenjun Qiu, David Lie, Lisa Austin
A significant challenge to training accurate deep learning models on privacy policies is the cost and difficulty of obtaining a large and comprehensive set of training data. To add…