VirtualIdentity: Privacy-Preserving User Profiling

Sisi Wang; Wing-Sea Poon; Golnoosh Farnadi; Caleb Horst; Kebra Thompson; Michael Nickels; Rafael Dowsley; Anderson C. A. Nascimento; Martine De Cock

arXiv · 1808.10151

VirtualIdentity: Privacy-Preserving User Profiling

Sisi Wang·Wing-Sea Poon·Golnoosh Farnadi·Caleb Horst·Kebra Thompson·Michael Nickels·Rafael Dowsley·Anderson C. A. Nascimento·Martine De Cock

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Abstract

User profiling from user generated content (UGC) is a common practice that supports the business models of many social media companies. Existing systems require that the UGC is fully exposed to the module that constructs the user profiles. In this paper we show that it is possible to build user profiles without ever accessing the user's original data, and without exposing the trained machine learning models for user profiling -- which are the intellectual property of the company -- to the users of the social media site. We present VirtualIdentity, an application that uses secure multi-party cryptographic protocols to detect the age, gender and personality traits of users by classifying their user-generated text and personal pictures with trained support vector machine models in a privacy-preserving manner.

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Sisi Wang, Wing-Sea Poon, Golnoosh Farnadi, Caleb Horst, Kebra Thompson, Michael Nickels, Rafael Dowsley, Anderson C. A. Nascimento, Martine De Cock. 2018-08-30. VirtualIdentity: Privacy-Preserving User Profiling. https://arxiv.org/abs/1808.10151

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