Cryptology ePrint Archive: Report 2019/744

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

Martine De Cock and Rafael Dowsley and Anderson C. A. Nascimento and Devin Reich and Ariel Todoki

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.

Category / Keywords: cryptographic protocols /

Date: received 24 Jun 2019

Contact author: rafael at dowsley net

Available format(s): PDF | BibTeX Citation

Version: 20190625:195549 (All versions of this report)

Short URL: ia.cr/2019/744


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