Related papers: Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

URL: http://arxiv.org/abs/2010.01285v1
Date: Sat, 3 Oct 2020 05:58:32 GMT
Title: Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness
Authors: Lingjuan Lyu, Xuanli He, Yitong Li
Abstract summary: It has been demonstrated that hidden representation learned by a deep model can encode private information of the input. We propose Differentially Private Neural Representation (DPNR) to preserve the privacy of the extracted representation from text.
Score: 38.90014773292902
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: It has been demonstrated that hidden representation learned by a deep model can encode private information of the input, hence can be exploited to recover such information with reasonable accuracy. To address this issue, we propose a novel approach called Differentially Private Neural Representation (DPNR) to preserve the privacy of the extracted representation from text. DPNR utilises Differential Privacy (DP) to provide a formal privacy guarantee. Further, we show that masking words via dropout can further enhance privacy. To maintain utility of the learned representation, we integrate DP-noisy representation into a robust training process to derive a robust target model, which also helps for model fairness over various demographic variables. Experimental results on benchmark datasets under various parameter settings demonstrate that DPNR largely reduces privacy leakage without significantly sacrificing the main task performance.

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