Online Advertising, Data Sharing, and Consumer Control

2021 ◽  
Author(s):  
Justin Johnson ◽  
Thomas Jungbauer ◽  
Marcel Preuss
Keyword(s):  
Data Sharing ◽  
Online Advertising ◽  
Consumer Control

scholarly journals Inferring Tracker-Advertiser Relationships in the Online Advertising Ecosystem using Header Bidding

2020 ◽  
Vol 2020 (1) ◽  
pp. 65-82
Author(s):  
John Cook ◽  
Rishab Nithyanand ◽  
Zubair Shafiq
Keyword(s):  
Machine Learning ◽  
Data Sharing ◽  
Online Advertising ◽  
Research Problem ◽  
Learning Model ◽  
Bidding Behavior ◽  
Server Side ◽  
Machine Learning Model ◽  
Novel Method ◽  
Client Side

AbstractOnline 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.

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