Characterising Third Party Cookie Usage in the EU after GDPR
May 03, 2019 Β· Declared Dead Β· π Web Science Conference
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Xuehui Hu, Nishanth Sastry
arXiv ID
1905.01267
Category
cs.CR: Cryptography & Security
Cross-listed
cs.IR
Citations
41
Venue
Web Science Conference
Last Checked
6 months ago
Abstract
The recently introduced General Data Protection Regulation (GDPR) requires that when obtaining information online that could be used to identify individuals, their consents must be obtained. Among other things, this affects many common forms of cookies, and users in the EU have been presented with notices asking their approvals for data collection. This paper examines the prevalence of third party cookies before and after GDPR by using two datasets: accesses to top 500 websites according to Alexa.com, and weekly data of cookies placed in users' browsers by websites accessed by 16 UK and China users across one year. We find that on average the number of third parties dropped by more than 10% after GDPR, but when we examine real users' browsing histories over a year, we find that there is no material reduction in long-term numbers of third party cookies, suggesting that users are not making use of the choices offered by GDPR for increased privacy. Also, among websites which offer users a choice in whether and how they are tracked, accepting the default choices typically ends up storing more cookies on average than on websites which provide a notice of cookies stored but without giving users a choice of which cookies, or those that do not provide a cookie notice at all. We also find that top non-EU websites have fewer cookie notices, suggesting higher levels of tracking when visiting international sites. Our findings have deep implications both for understanding compliance with GDPR as well as understanding the evolution of tracking on the web.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Cryptography & Security
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
The Limitations of Deep Learning in Adversarial Settings
R.I.P.
π»
Ghosted
Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks
R.I.P.
π»
Ghosted
Spectre Attacks: Exploiting Speculative Execution
R.I.P.
π»
Ghosted
How To Backdoor Federated Learning
R.I.P.
π»
Ghosted
Evasion Attacks against Machine Learning at Test Time
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted