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Abstract

This paper firstly proposes a simple yet efficient generalized approach to apply differential privacy to text representation (i.e., word embedding). Based on it, we propose a user-level approach to learn personalized differentially private word embedding model on user generated contents (UGC). To our best knowledge, this is the first work of learning user-level differentially private word embedding model from text for sharing. The proposed approaches protect the privacy of the individual from re-identification, especially provide better trade-off of privacy and data utility on UGC data for sharing. The experimental results show that the trained embedding models are applicable for the classic text analysis tasks (e.g., regression). Moreover, the proposed approaches of learning difierentially private embedding models are both framework- and dataindependent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.

Item Type:	Conference or Workshop Item (Paper)
Authors/Creators:	Vu, X-S and Tran, SN and Jiang, L
Keywords:	privacy, generated text, private word embedding, differential privacy, UGC
Journal or Publication Title:	Proceedings of the 20th International Conference on Computational Linguistics and Intelligent Text Processing
Copyright Information:	Copyright unknown
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