Active learning holds promise of significantly reducing data annotation costs
while maintaining reasonable model performance. However, it requires sending
data to annotators for labeling. This presents a possible privacy leak when the
training set includes sensitive user data. In this paper, we describe an
approach for carrying out privacy preserving active learning with quantifiable
guarantees. We evaluate our approach by showing the tradeoff between privacy,
utility and annotation budget on a binary classification task in a active
learning setting.Comment: To appear at PAL: Privacy-Enhancing Artificial Intelligence and
Language Technologies as part of the AAAI Spring Symposium Series (AAAI-SSS
2019
