Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation: An Application to Hate-Speech Detection
This addresses privacy concerns for users in applications like surveillance and mental health care by enabling secure text classification, though it is incremental as it applies existing SMC methods to text data.
The paper tackles the problem of privacy violations in personal text message classification by proposing the first provably secure solution using Secure Multiparty Computation (SMC), achieving excellent runtime results without loss of accuracy in hate-speech detection applications.
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.