Ad-blocker has become an increasing concern of the web services that are largely reliant on advertising revenues. Such web services operate with the implicit assumption that users agree to watch ads to support these “free” services. Unfortunately, the economic magnetism of online advertising has made it an attractive target for various types of abuses, which are driven by incentives for higher monetary benefits (e.g., drive-by downloads, overly annoying ads). Ad-blocking software can seamlessly block ads without requiring any user input, which not only improves the web experience but also protects user privacy by filtering network requests that profile browsing behaviors.
The advertising industry sees ad-blocker as a growing threat to their business model and therefore has started fighting back with ad-block detection capabilities. The idea is that the scripts can detect the presence of ad-blockers and refuse to serve users who use ad-blockers. Many popular websites such as The Guardian, WIRED, and Forbes have recently started interrupting and/or blocking visitors who use ad-blockers. The ongoing arms race between ad-blocker and ad-block detectors has a significant impact on the future of user privacy and the way the Internet advertising industry operates. Yet, little is known in terms of the scale and technical details of the arms race between ad-blockers and ad-block detectors.
In this proposal, we plan to undertake two major research tasks: First, we will perform a systematic measurement and analysis of the ad-block detection phenomenon on the web. This involves understanding how many websites are performing ad-block detection; and what type of technical approaches are used. Second, from the gained understanding, we aim to design and implement new mechanisms, representing the next step in the arms race, in the form of a stealthy or invisible ad-blocker to counter or circumvent ad-block detection. All of the produced data and software will be shared publicly.