On the regulation of personal data distribution in online advertising platforms

Abstract

Online tracking is the key enabling technology of modern online advertising. In the recently established model of real-time bidding (RTB), the web pages tracked by ad platforms are shared with advertising agencies (also called DSPs), which, in an auction-based system, may bid for user ad impressions. Since tracking data are no longer confined to ad platforms, RTB poses serious risks to privacy, especially with regard to user profiling, a practice that can be conducted at a very low cost by any DSP or related agency, as we reveal here. In this work, we illustrate these privacy risks by examining a data set with the real ad-auctions of a DSP, and show that for at least 55% of the users tracked by this agency, it paid nothing for their browsing data. To mitigate this abuse, we propose a system that regulates the distribution of bid requests (containing user tracking data) to potentially interested bidders, depending on their previous behavior. In our approach, an ad platform restricts the sharing of tracking data by limiting the number of DSPs participating in each auction, thereby leaving unchanged the current RTB architecture and protocols. However, doing so may have an evident impact on the ad platform's revenue. The proposed system is designed accordingly, to ensure the revenue is maximized while the abuse by DSPs is prevented to a large degree. Experimental results seem to suggest that our system is able to correct misbehaving DSPs, and consequently enhance user privacy.

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