VirtualIdentity: Privacy-Preserving User Profiling
This addresses privacy concerns for social media users and intellectual property protection for companies, representing a novel approach to a known bottleneck.
The paper tackles the problem of user profiling from user-generated content without exposing original data or proprietary models, achieving privacy-preserving detection of age, gender, and personality traits using secure multi-party cryptographic protocols.
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.