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Widely reported privacy issues concerning major online advertising platforms (e.g., Facebook) have heightened concerns among users about the data that is collected about them. However, while we have a comprehensive understanding who collects data on users, as well as how tracking is implemented, there is still a significant gap in our understanding: what information do advertisers actually infer about users, and is this information accurate? In this study, we leverage Ad Preference Managers (APMs) as a lens through which to address this gap. APMs are transparency tools offered by some advertising platforms that allow users to see the interest profiles that are constructed about them. We recruited 220 participants to install an IRB approved browser extension that collected their interest profiles from four APMs (Google, Facebook, Oracle BlueKai, and Neilsen eXelate), as well as behavioral and survey data. We use this data to analyze the size and correctness of interest profiles, compare their composition across the four platforms, and investigate the origins of the data underlying these profiles.

Advertising and Analytics (A&A) companies have started collaborating more closely with one another due to the shift in the online advertising industry towards Real Time Bidding (RTB). One natural way to understand how user tracking data moves through this interconnected advertising ecosystem is by modeling it as a graph. In this paper, we introduce a novel graph representation, called an Inclusion graph, to model the impact of RTB on the diffusion of user tracking data in the advertising ecosystem. Through simulations on the Inclusion graph, we provide upper and lower estimates on the tracking information observed by A&A companies. We find that 52 A&A companies observe at least 91% of an average user’s browsing history under reasonable assumptions about information sharing within RTB auctions. We also evaluate the effectiveness of blocking strategies (e.g., AdBlock Plus), and find that major A&A companies still observe 40–90% of user impressions, depending on the blocking strategy.

One advertising format that has grown significantly in recent years are known as Content Recommendation Networks (CRNs). CRNs are responsible for the widgets full of links that appear under headlines like "Recommended For You" and "Things You Might Like". Although CRNs have become quite popular with publishers, users complain about the low-quality of content promoted by CRNs, while regulators in the US and Europe have faulted CRNs for failing to label sponsored links as advertisements. In this study, we present a first look at five of the largest CRNs, including their footprint on the web, how their recommendations are labeled, and who their advertisers are. Our findings reveal that CRNs still fail to prominently disclose the paid nature of their sponsored content. This suggests that additional intervention is necessary to promote accepted best-practices in the nascent CRN marketplace, and ultimately protect online users.