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Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation
Devin Reich · Ariel Todoki · Rafael Dowsley · Martine De Cock · Anderson Nascimento

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #96

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.

Author Information

Devin Reich (University of Washington Tacoma)
Ariel Todoki (University of Washington Tacoma)
Rafael Dowsley (Bar-Ilan University)
Martine De Cock (University of Washington Tacoma)
Anderson Nascimento (UW)

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