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

Understand

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.