Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding
Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding
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DOI：
10.2478/popets-2020-0001
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发表时间：
2019-07
影响因子：
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通讯作者：
John Cook;Rishab Nithyanand;Zubair Shafiq
中科院分区：
文献类型：
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作者：
John Cook;Rishab Nithyanand;Zubair Shafiq
在线广告依赖于跟踪器和数据代理向用户显示有针对性的广告。为了提高目标定位，错综复杂的在线广告和跟踪生态系统中的不同实体被激励通过客户端或服务器端机制彼此共享信息。实体间数据共享的推断，尤其是当它发生在服务器端时，是一个重要且具有挑战性的研究问题。在本文中，我们介绍Kashf：一种新的方法来推断广告商和跟踪器之间的数据共享关系，通过研究广告商的出价行为如何随着我们操纵跟踪器的存在而变化。我们通过训练一个可解释的机器学习模型来实现这一洞察力，该模型使用跟踪器的存在作为特征来预测广告商的出价行为。通过分析机器学习模型，我们可以推断广告商和跟踪者之间的关系，而不管数据共享是发生在客户端还是服务器端。我们能够识别出几个服务器端的数据共享关系，这些关系在外部进行了验证，但客户端的cookie同步却没有检测到。
Abstract Online advertising relies on trackers and data brokers to show targeted ads to users. To improve targeting, different entities in the intricately interwoven online advertising and tracking ecosystems are incentivized to share information with each other through client-side or server-side mechanisms. Inferring data sharing between entities, especially when it happens at the server-side, is an important and challenging research problem. In this paper, we introduce Kashf: a novel method to infer data sharing relationships between advertisers and trackers by studying how an advertiser’s bidding behavior changes as we manipulate the presence of trackers. We operationalize this insight by training an interpretable machine learning model that uses the presence of trackers as features to predict the bidding behavior of an advertiser. By analyzing the machine learning model, we can infer relationships between advertisers and trackers irrespective of whether data sharing occurs at the client-side or the server-side. We are able to identify several server-side data sharing relationships that are validated externally but are not detected by client-side cookie syncing.