Title
Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Publication Date
2019
Document Type
Article
Abstract
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.
Publication Title
Advances in Neural Information Processing Systems 32
First Page
3752
Last Page
3764
Publisher Policy
No SHERPA/RoMEO policy available
Open Access Status
Licensed
Recommended Citation
Reich, D., Todoki, A., Dowsley, R., De Cock, M., & Nascimento, A. (2019). Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation. In H. Wallach, H. Larochelle, A. Beygelzimer, F. d\textquotesingle Alché-Buc, E. Fox, & R. Garnett (Eds.), Advances in Neural Information Processing Systems 32 (pp. 3752–3764). http://papers.nips.cc/paper/8632-privacy-preserving-classification-of-personal-text-messages-with-secure-multi-party-computation.pdf
