Machine Learning for Browser Privacy

Abstract

Online privacy is an Internet user’s control of how much personal information is shared with a third party. Unfortunately, some third parties, such as data brokers, collect user data without permission to resell the data to other parties. Browser tracking allows each Internet user to be uniquely identified, and in-depth user profiles are built. Browser fingerprinting is one of the most effective methods of browser tracking. It uniquely identifies each user through their devices’ configuration, even for users using the same device models. Using Virtual Private Networks, the Tor browser and specific browser extensions as a countermeasure against browser fingerprinting are not widespread, so it often results in a compromised user experience. Researchers have proposed various classification machine learning approaches to improve browser privacy; some focus on recognising and blocking advertisements and website scripts that track users. In contrast, others identify potential vulnerabilities in browser security configurations. There is a need for more research in machine learning, especially natural language processing, to enhance browser privacy.