dpUGC: Learn Differentially Private Representation for User Generated Contents
This work addresses privacy concerns for users sharing text data, offering a novel method for personalized differential privacy in embeddings, though it is incremental in applying existing privacy techniques to a specific domain.
The paper tackles the problem of learning differentially private word embeddings from user-generated content, proposing a user-level approach that protects individual privacy while maintaining data utility. Experimental results show the models are effective for text analysis tasks like regression.
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 differentially private embedding models are both framework- and data- independent, which facilitates the deployment and sharing. The source code is available at https://github.com/sonvx/dpText.