# dpUGC: Learn Differentially Private Representation for User Generated Contents

arXiv: https://arxiv.org/abs/1903.10453 · HTML: https://mlsanity.com/paper/1903.10453/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Mar 2019.

## Built on by (most cited first, 5 of 5)

- [CAPE: Context-Aware Private Embeddings for Private Language Learning](https://mlsanity.com/paper/2108.12318/) (2021)
- [Wide Network Learning with Differential Privacy](https://mlsanity.com/paper/2103.01294/) (2021)
- [Guiding Text-to-Text Privatization by Syntax](https://mlsanity.com/paper/2306.01471/) (2023)
- [On the privacy-utility trade-off in differentially private hierarchical text classification](https://mlsanity.com/paper/2103.02895/) (2021)
- [Federated Artificial Intelligence for Unified Credit Assessment](https://mlsanity.com/paper/2105.09484/) (2021)

## Abstract (arXiv)

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.