ATOM: A Generalizable Technique for Inferring Tracker-Advertiser Data Sharing in the Online Behavioral Advertising Ecosystem (2207.10791v1)

Abstract: Data sharing between online trackers and advertisers is a key component in online behavioral advertising. This sharing can be facilitated through a variety of processes, including those not observable to the user's browser. The unobservability of these processes limits the ability of researchers and auditors seeking to verify compliance with regulations which require complete disclosure of data sharing partners. Unfortunately, the applicability of existing techniques to make inferences about unobservable data sharing relationships is limited due to their dependence on protocol- or case-specific artifacts of the online behavioral advertising ecosystem (e.g., they work only when client-side header bidding is used for ad delivery or when advertisers perform ad retargeting). As behavioral advertising technologies continue to evolve rapidly, the availability of these artifacts and the effectiveness of transparency solutions dependent on them remain ephemeral. In this paper, we propose a generalizable technique, called ATOM, to infer data sharing relationships between online trackers and advertisers. ATOM is different from prior work in that it is universally applicable -- i.e., independent of ad delivery protocols or availability of artifacts. ATOM leverages the insight that by the very nature of behavioral advertising, ad creatives themselves can be used to infer data sharing between trackers and advertisers -- after all, the topics and brands showcased in an ad are dependent on the data available to the advertiser. Therefore, by selectively blocking trackers and monitoring changes in the characteristics of ads delivered by advertisers, ATOM is able to identify data sharing relationships between trackers and advertisers. The relationships discovered by our implementation of ATOM include those not found using prior approaches and are validated by external sources.