Preprint
Turning the Tide on Dark Pools? Towards Multi-Stakeholder Vulnerability Notifications in the Ad-Tech Supply Chain
arXiv.org
Cornell University
06/11/2024
DOI: 10.48550/arxiv.2406.06958
Abstract
Online advertising relies on a complex and opaque supply chain that involves multiple stakeholders, including advertisers, publishers, and ad-networks, each with distinct and sometimes conflicting incentives. Recent research has demonstrated the existence of ad-tech supply chain vulnerabilities such as dark pooling, where low-quality publishers bundle their ad inventory with higher-quality ones to mislead advertisers. We investigate the effectiveness of vulnerability notification campaigns aimed at mitigating dark pooling. Prior research on vulnerability notifications has primarily focused on single-stakeholder scenarios, and it is unclear whether vulnerability notifications can be effective in the multi-stakeholder ad-tech supply chain. We implement an automated vulnerability notification pipeline to systematically evaluate the responsiveness of various stakeholders, including publishers, ad-networks, and advertisers to vulnerability notifications by academics and activists. Our nine-month long multi-stakeholder notification study shows that notifications are an effective method for reducing dark pooling vulnerabilities in the online advertising ecosystem, especially when targeted towards ad-networks. Further, the sender reputation does not impact responses to notifications from activists and academics in a statistically different way. In addition to being the first notification study targeting the online advertising ecosystem, we are also the first to study multi-stakeholder context in vulnerability notifications.
Details
- Title: Subtitle
- Turning the Tide on Dark Pools? Towards Multi-Stakeholder Vulnerability Notifications in the Ad-Tech Supply Chain
- Creators
- Yash Vekaria - University of California, DavisRishab Nithyanand - University of IowaZubair Shafiq - University of California, Davis
- Resource Type
- Preprint
- Publication Details
- arXiv.org
- Publisher
- Cornell University; Ithaca, New York
- DOI
- 10.48550/arxiv.2406.06958
- eISSN
- 2331-8422
- Language
- English
- Date posted
- 06/11/2024
- Academic Unit
- Law Faculty; Computer Science; Center for Social Science Innovation
- Record Identifier
- 9984643648802771
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