In-Depth Technical and Legal Analysis of Tracking on Health Related Websites with ERNIE Extension
In-Depth Technical and Legal Analysis of Tracking on Health Related Websites with ERNIE Extension
复制标题
使用 ERNIE 扩展对健康相关网站进行跟踪的深入技术和法律分析
DOI:
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发表时间:
2021
期刊:
影响因子:
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通讯作者:
A. Legout
中科院分区:
文献类型:
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作者:
Vera Wesselkamp;Imane Fouad;C. Santos;Yanis Boussad;Nataliia Bielova;A. Legout
Searching the Web to find doctors and make appointments online is a common practice nowadays. However, simply visiting a doctors website might disclose health related information. As the GDPR only allows processing of health data with explicit user consent, health related websites must ask consent before any data processing, in particular when they embed third party trackers.Admittedly, it is very hard for owners of such websites to both detect the complex tracking practices that exist today and to ensure legal compliance. In this paper, we present ERNIE, a browser extension we designed to visualise six state-of-the-art tracking techniques based on cookies. Using ERNIE, we analysed 385 health related websites that users would visit when searching for doctors in Germany, Austria, France, Belgium, and Ireland. More specifically, we explored the tracking behavior before any interaction with the consent pop-up and after rejection of cookies on websites of doctors, hospitals, and health related online phone-books. We found that at least one form of tracking occurs on 62% of the websites before interacting with the consent pop-up, and 15% of websites include tracking after rejection. Finally, we performed a detailed technical and legal analysis of three health related websites that demonstrate impactful legal violations. This paper shows that while, from a legal point of view, health related websites are more privacy-sensitive than other kinds of websites, they are exposed to the same technical difficulties to implement a legally compliant website. We believe ERNIE, the browser extension we developed, to be an invaluable tool for policy-makers and regulators to improve detection and visualization of the complex tracking techniques used on these websites.
DOI:
10.1145/3411764.3445779
发表时间:
2021
期刊:
CHI '21: Proceedings of the 2021 CHI Conference on Human Factors in Computing
影响因子:
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作者:
Gray, Colin M.;Santos, Cristiana;Bielova, Nataliia;Toth, Michael;Clifford, Damian
通讯作者:
Clifford, Damian