Abstract
Although digital advertising fuels much of today's free Web, it typically do so at the cost of online users' privacy, due to continuous tracking and leakage of users' personal data. In search for new ways to optimize effectiveness of ads, advertisers have introduced new paradigms such as cross-device tracking (CDT), to monitor users' browsing on multiple screens, and deliver (re)targeted ads in the appropriate screen. Unfortunately , this practice comes with even more privacy concerns for the end-user. In this work, we design a methodology for triggering CDT by emulating realistic browsing activity of end-users, and then detecting and measuring it by leveraging advanced machine learning tools.