MindYourPrivacy: Design and implementation of a visualization system for third-party Web tracking

Third-party Web tracking is a serious privacy issue. Advertisement sites and social networking sites stealthily collect users' Web browsing history for purposes such as targeted advertising or predicting trends. Unfortunately, very few Internet users realize this, and their privacy has been infringed upon because they have no means of recognizing the situation. In this paper we present the design and implementation of a system called MindYourPrivacy that visualizes third-party Web tracking and clarifies the entities threatening users' privacy. The implementation adopts deep packet inspection, DNS-SOA-record-based categorization, and HTTP-referred graphical analysis to visualize collectors of Web browsing histories without device dependency. To demonstrate the effectiveness of our proof-of-concept implementation, we conducted an experiment in an IT technology camp, where 129 attendees discussed IT technologies for four days, The experiment's results revealed that visualizing Web tracking effectively influences users' perception of privacy. Analysis of the user data we collected at the camp also revealed that MCODE clustering and some features derived from graph theory are useful for detecting advertising sites that potentially collect user information by Web tracking for their own purposes.