dpUGC: Learn Differentially Private Representation for User Generated Contents

TL;DR

This paper introduces a novel method for learning user-level differentially private word embeddings from user-generated content, balancing privacy protection with utility for text analysis tasks.

Contribution

It presents the first user-level differentially private word embedding approach applicable to sharing UGC data, enhancing privacy without sacrificing utility.

Findings

Embeddings are effective for text analysis tasks like regression.

The approach is framework- and data-independent, facilitating deployment.

Provides better privacy-utility trade-offs on UGC data.

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 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.