MyAdChoices

Abstract

The intrusiveness and the increasing invasiveness of online advertising have, in the last few years, raised serious concerns regarding user privacy and Web usability. As a reaction to these concerns, we have witnessed the emergence of a myriad of ad-blocking and antitracking tools, whose aim is to return control to users over advertising. The problem with these technologies, however, is that they are extremely limited and radical in their approach: users can only choose either to block or allow all ads. With around 200 million people regularly using these tools, the economic model of the Web—in which users get content free in return for allowing advertisers to show them ads—is at serious peril. In this article, we propose a smart Web technology that aims at bringing transparency to online advertising, so that users can make an informed and equitable decision regarding ad blocking. The proposed technology is implemented as a Web-browser extension and enables users to exert fine-grained control over advertising, thus providing them with certain guarantees in terms of privacy and browsing experience, while preserving the Internet economic model. Experimental results in a real environment demonstrate the suitability and feasibility of our approach, and provide preliminary findings on behavioral targeting from real user browsing profiles.

The Representativeness of Automated Web Crawls as a Surrogate for Human Browsing

“…Papadopoulos et al use a dataset collected over 1 year from 850 participants and explicitly state they are not affected by distortions that exist in crawled data [45]. Another approach is to collect data through websites that provide information and feedback to users to entice them to participate [16,26,42], as well as apps and browser extensions [46,61,62]. Crowd-sourcing services can also be used to pay users to perform specific actions [29].…”

My Mouse, My Rules: Privacy Issues of Behavioral User Profiling via Mouse Tracking

“…Of course, user profiling is not a bad thing per se, since it allows to deliver more relevant ad content to the user. However, as targeted ads are believed to produce increased revenues, various intermediary companies (such as ad platforms and ad exchanges) are tracking users at scale, and often in an unregulated manner, which has resulted in a privacy nightmare [77] 1: Test performance of our BiGRU when trained using adversarial noise 𝝈 ∼ U (0, 1). We show the performance degradation in parentheses, as a the percentage variation w.r.t the same classifier trained on non-distorted data.…”

“…There is a plethora of work that capitalize on user profiling based on how we browse. For example, our browsing history is used to detect targeted ads [21,77] or even identify which attribute or user action triggered a specific ad [55,101]. Olejnik et al [75] noticed that, with just 4 visited websites, it is possible to uniquely identify users in 97% of cases.…”

An Adversarial-Risk-Analysis Approach to Counterterrorist Online Surveillance

“…The purpose of online third-party tracking is behavioral advertising [10,11], that is to say, showing ads based on the user’s past browsing activity. In this section, we first give a brief overview of the main actors of the advertising ecosystem.…”