MetaPriv: Acting in Favor of Privacy on Social Media Platforms

Abstract

Social networks such as Facebook (FB) and Instagram are known for tracking user online behaviour for commercial gain. To this day, there is practically no other way of achieving privacy in said platforms other than renouncing their use. However, many users are reluctant in doing so because of convenience or social and professional reasons. In this work, we propose a means of balancing convenience and privacy on FB through obfuscation. We have created MetaPriv, a tool based on simulating user interaction with FB. MetaPriv allows users to add noise interactions to their account so as to lead FB's profiling algorithms astray, and make them draw inaccurate profiles in relation to their interests and habits. To prove our tool's effectiveness, we ran extensive experiments on a dummy account and two existing user accounts. Our results showed that, by using our tool, users can achieve a higher degree of privacy in just a couple of weeks. We believe that MetaPriv can be further developed to accommodate other social media platforms and help users regain their privacy, while maintaining a reasonable level of convenience. To support open science and reproducible research, our source code is publicly available online.