Karel Kubíček's site

CookieBlock interface

Automating Cookie Consent and GDPR Violation Detection

Authors: Dino Bollinger, Karel Kubicek karel.kubicek@inf.ethz.ch, Carlos Cotrini, David Basin

Abstract: The European Union’s General Data Protection Regulation (GDPR) requires websites to inform users about personal data collection and request consent for cookies. Yet the majority of websites do not give users any choices, and others attempt to deceive them into accepting all cookies. We document the severity of this situation through an analysis of potential GDPR violations in cookie banners in almost 30k websites. We identify six novel violation types, such as incorrect category assignments and misleading expiration times, and we find at least one potential violation in a surprising 94.7% of the analyzed websites.

We address this issue by giving users the power to protect their privacy. We develop a browser extension, called CookieBlock, that uses machine learning to enforce GDPR cookie consent at the client. It automatically categorizes cookies by usage purpose using only the information provided in the cookie itself. At a mean validation accuracy of 84.4%, our model attains a prediction quality competitive with expert knowledge in the field. Additionally, our approach differs from prior work by not relying on the cooperation of websites themselves. We empirically evaluate CookieBlock on a set of 100 randomly sampled websites, on which it filters roughly 90% of the privacy-invasive cookies without significantly impairing website functionality.

BibTeX

@inproceedings{bollinger2022automating,
  author = {Dino Bollinger and Karel Kubicek and Carlos Cotrini and David Basin},
  title = {Automating Cookie Consent and {GDPR} Violation Detection},
  booktitle = {31st USENIX Security Symposium (USENIX Security 22)},
  year = {2022},
  month = aug,
  pages = {2893--2910},
  isbn = {978-1-939133-31-1},
  publisher = {USENIX Association},
  url = {https://www.usenix.org/conference/usenixsecurity22/presentation/bollinger},
  address = {Boston, MA},
}

Browser cookies are one of the most commonly used methods for tracking the session state of websites, and for tracking the identity of visitors. According to prior studies, between 80-90% of websites use cookies for user tracking, often without their knowledge. The EU government has attempted to address this issue through regulations mandating consent for data collection, in particular through the General Data Protection Regulation (GDPR) and the ePrivacy Directive.

Despite these requirements, prior research has shown that less than half of all websites ask visitors for consent. Of the remaining websites, many violate even basic consent requirements. For instance, the majority of websites did not adhere to the opt-in requirement, and more than 10% stored affirmative consent before visitors could react to the consent notice. Some websites also stored affirmative consent despite explicit rejection, and many consent notices use dark patterns to nudge users into accepting all cookies.

In our analysis, we confirm the lack of GDPR compliance by extending and improving upon past research. We analyze the accuracy of the information displayed on cookie banners using a dataset collected from almost 30k websites. Specifically, we identify incorrect category assignments, misleading cookie expiration times, and assess the overall completeness of the consent mechanism. We define six novel methods to detect potential GDPR violations and extend two methods used in prior works. Of the selected domains, we find that 94.7% contained at least one potential violation. In 36.4%, we found at least one cookie with an incorrectly assigned purpose, and in 85.8%, there was at least one cookie with a missing declaration or missing purpose. 69.7% of sites assumed positive consent before it is given, and 21.3% created cookies despite negative consent. Our results indicate that consent notices are less compliant than previous research indicated.

Violation types The number of websites that show the respective type of violation. The first six are novel and have not been explored in prior work.

We support the legal claims by referring to relevant sections of GPDR and ePrivacy Directive and Planet49 case ruled by the EU Court of Justice.

Histogram of violations This histogram shows the distribution of violation types per website, with the green bar representing the compliant ones. It does not include repetitions of a single type.

For the case of missing cookie declarations or purposes, we argue that the issues stem from neglect rather than malice. The cause is likely the lack of enforcement and web administrators who are not sufficiently familiar with the legal requirements. These violations can be addressed by providing regulatory authorities crawlers used in this study to improve enforcement of the GDPR. If you are a regulatory authority or any legal body interested in our work, please contact the authors.

Client-side mitigation with extension CookieBlock

According to evidence from prior works and our own measurements, cookie consent practices are so often in violation of the GDPR that regulatory authorities cannot hope to keep up. Therefore, we provide users with a tool to enforce cookie consent on their web clients without requiring regulations to be enforced. We develop the browser extension CookieBlock, which classifies cookies by purpose, deleting those that the user rejects. In this way, the user can remove over 90% of all privacy-invasive cookies, without having to trust cookie banners. Previous attempts to provide users such control, like the P3P standard, failed due to a lack of willingness of website administrators to implement the functionality. We sidestep this problem by not relying on the cooperation of the websites at all.

In order to train the classifier model, we collected a dataset of approximately 304k cookies with purpose labels from 30k websites using selected Consent Management Platforms (CMP) to display the consent notice including cookie to purpose mapping. We retrieve the purposes the cookies are assigned to, and match these to the actual cookies that are created in the browser while visiting the website.

From the collected cookies, we extract statistically-rich, domain-specific features for each cookie. These features are determined from multiple attributes, including the name, domain, path, value, expiration date, as well as flags such as the “HttpOnly,” “Secure,” “SameSite,” and “HostOnly” properties.

For cookie classification, CookieBlock uses an ensemble of decision trees model, which is trained using the XGBoost library. We evaluate the model by comparing its performance to that of the Cookiepedia repository. Cookiepedia assigns purposes to cookies based on their name and was constructed manually for 10 years by human operators. We query this repository for purpose predictions and compare the results to the ground truth. In summary, we find that Cookiepedia achieves a balanced accuracy of 84.7%, while our XGBoost model achieves 84.4%. As such, our model is competitive with human expertise, showing that it is possible to automatically classify cookies by purpose using only the information available in the cookies themselves.

Cookiepedia performanceOur XGBoost model performance

Confusion matrices Performance comparison of Cookiepedia to our automated XGBoost model, showing that our model is competitive with human expertise.

We evaluate CookieBlock on a set of 100 websites to quantify the impact the extension has on the browsing experience. CookieBlock causes no issues on 85% of the sites, minor problems involving non-essential website functions on 8%, and more substantial defects on 7%. The latter involve the user’s login status being lost due to the cookie removal. To resolve these problems, the user can selectively define website exemptions, and change the classification of cookies through CookieBlock’s interface.

CookieBlock interface CookieBlock interface consists of a simple popup and settings.

CookieBlock, the cookie purpose classification model, and the web crawler can help various parties:

Errata of paper (addressed in our version)

In Section 6.4, we claim “This aligns with the results by Nouwens et al. [39], who found that 67.6% of 680 sites used implicit consent” However, Nouwens et al. found the violations at 32.5% of websites, therefore our results are not aligned with theirs. This is caused by entirely different sample of websites, where Nouwens et al. has a generic sample of websites while we focus only on websites with selected CMPs.

In section 4.1, we reported that Cookiepedia achieves 83.4% ballanced accuracy, while in the final measurement of our work, we observed 84.7%.

Q&A for users

Q&A for developers

Q&A for researchers/others

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Acknowledgement

The authors would like to thank:

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