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Active learning holds promise of significantly reducing data annotation costswhile maintaining reasonable model performance. However, it requires sendingdata to annotators for labeling. This presents a possible privacy leak when thetraining set includes sensitive user data. In this paper, we describe anapproach for carrying out privacy preserving active learning with quantifiableguarantees. We evaluate our approach by showing the tradeoff between privacy,utility and annotation budget on a binary classification task in a activelearning setting.

Privacy-preserving Active Learning on Sensitive Data for User Intent Classification