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arXiv · 2006.14170

Towards Differentially Private Text Representations

Lingjuan Lyu·Yitong Li·Xuanli He·Tong Xiao
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Abstract

Most deep learning frameworks require users to pool their local data or model updates to a trusted server to train or maintain a global model. The assumption of a trusted server who has access to user information is ill-suited in many applications. To tackle this problem, we develop a new deep learning framework under an untrusted server setting, which includes three modules: (1) embedding module, (2) randomization module, and (3) classifier module. For the randomization module, we propose a novel local differentially private (LDP) protocol to reduce the impact of privacy parameter $\epsilon$ on accuracy, and provide enhanced flexibility in choosing randomization probabilities for LDP. Analysis and experiments show that our framework delivers comparable or even better performance than the non-private framework and existing LDP protocols, demonstrating the advantages of our LDP protocol.

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BibTeXRIS

Lingjuan Lyu, Yitong Li, Xuanli He, Tong Xiao. 2020-06-25. Towards Differentially Private Text Representations. https://arxiv.org/abs/2006.14170

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