# Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection

arXiv: https://arxiv.org/abs/1906.02325 · HTML: https://mlsanity.com/paper/1906.02325/

## Key facts

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

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

- [Privacy-Preserving Training of Tree Ensembles over Continuous Data](https://mlsanity.com/paper/2106.02769/) (2021)
- [PrivFair: a Library for Privacy-Preserving Fairness Auditing](https://mlsanity.com/paper/2202.04058/) (2022)
- [Fast Privacy-Preserving Text Classification based on Secure Multiparty Computation](https://mlsanity.com/paper/2101.07365/) (2021)
- [Enhancing Privacy in the Early Detection of Sexual Predators Through Federated Learning and Differential Privacy](https://mlsanity.com/paper/2501.12537/) (2025)
- [Round and Communication Balanced Protocols for Oblivious Evaluation of Finite State Machines](https://mlsanity.com/paper/2103.11240/) (2021)

## Abstract (arXiv)

Classification of personal text messages has many useful applications in surveillance, e-commerce, and mental health care, to name a few. Giving applications access to personal texts can easily lead to (un)intentional privacy violations. We propose the first privacy-preserving solution for text classification that is provably secure. Our method, which is based on Secure Multiparty Computation (SMC), encompasses both feature extraction from texts, and subsequent classification with logistic regression and tree ensembles. We prove that when using our secure text classification method, the application does not learn anything about the text, and the author of the text does not learn anything about the text classification model used by the application beyond what is given by the classification result itself. We perform end-to-end experiments with an application for detecting hate speech against women and immigrants, demonstrating excellent runtime results without loss of accuracy.