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Found 4 papers, 1 papers with code
Leveraging Hierarchical Representations for Preserving Privacy and Utility in Text
In this work, we explore word representations in Hyperbolic space as a means of preserving privacy in text.
Privacy- and Utility-Preserving Textual Analysis via Calibrated Multivariate Perturbations
We conduct privacy audit experiments against 2 baseline models and utility experiments on 3 datasets to demonstrate the tradeoff between privacy and utility for varying values of epsilon on different task types.
Privacy-preserving Active Learning on Sensitive Data for User Intent Classification
Active learning holds promise of significantly reducing data annotation costs while maintaining reasonable model performance.
Leveraging Crowdsourcing Data For Deep Active Learning - An Application: Learning Intents in Alexa
Experiments show that our framework can accurately learn annotator expertise, infer true labels, and effectively reduce the amount of annotations in model training as compared to state-of-the-art approaches.
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