Multifaceted Privacy: How to Express Your Online Persona without Revealing Your Sensitive Attributes
Victor Zakhary, Ishani Gupta, Rey Tang, Amr El Abbadi

TL;DR
This paper introduces Multifaceted privacy, a new model and system that enables users to publicly share their online personas while obfuscating sensitive attributes through suggested posts, enhancing privacy in social networks.
Contribution
It proposes a novel privacy model and develops Aegis, a system that helps users control and obfuscate sensitive attributes without compromising their public persona.
Findings
Adding 0 to 4 obfuscation posts effectively hides sensitive attributes.
The system preserves public persona attributes while obfuscating private ones.
Few posts are needed to achieve privacy without altering the user's public profile.
Abstract
Recent works in social network stream analysis show that a user's online persona attributes (e.g., gender, ethnicity, political interest, location, etc.) can be accurately inferred from the topics the user writes about or engages with. Attribute and preference inferences have been widely used to serve personalized recommendations, directed ads, and to enhance the user experience in social networks. However, revealing a user's sensitive attributes could represent a privacy threat to some individuals. Microtargeting (e.g.,Cambridge Analytica scandal), surveillance, and discriminating ads are examples of threats to user privacy caused by sensitive attribute inference. In this paper, we propose Multifaceted privacy, a novel privacy model that aims to obfuscate a user's sensitive attributes while publicly preserving the user's public persona. To achieve multifaceted privacy, we build Aegis,…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Privacy, Security, and Data Protection · Data-Driven Disease Surveillance
