User Perception and Actions Through Risk Analysis Concerning Cookies
- URL: http://arxiv.org/abs/2211.07366v1
- Date: Mon, 14 Nov 2022 14:02:50 GMT
- Title: User Perception and Actions Through Risk Analysis Concerning Cookies
- Authors: Matthew Wheeler, Suleiman Saka and Sanchari Das
- Abstract summary: We conducted a user study through a control versus experimental group survey.
Our goal was to gauge how user knowledge reflected their security and privacy preferences on the internet.
We analyzed user awareness of cookies, their privacy implications, and how risk communication can impact user behavior.
- Score: 1.2891210250935146
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: A website browser cookie is a small file created by a web server upon
visitation, which is placed in the user's browser directory to enhance the
user's experience. However, first and third-party cookies have become a
significant threat to users' privacy due to their data collection methods. To
understand the users' perception of the risk of cookies and targeted
advertisements, we conducted a user study through a control versus experimental
group survey. Our goal was to gauge how user knowledge reflected their security
and privacy preferences on the internet; thus, for the experimental group, we
created a learning website and information videos through participatory design
in a workshop with 15 participants. After that, by evaluating the responses of
68 participants through the survey, we analyzed user awareness of cookies,
their privacy implications, and how risk communication can impact user
behavior.
Related papers
- Fingerprinting and Tracing Shadows: The Development and Impact of Browser Fingerprinting on Digital Privacy [55.2480439325792]
Browser fingerprinting is a growing technique for identifying and tracking users online without traditional methods like cookies.
This paper gives an overview by examining the various fingerprinting techniques and analyzes the entropy and uniqueness of the collected data.
arXiv Detail & Related papers (2024-11-18T20:32:31Z) - How Unique is Whose Web Browser? The role of demographics in browser fingerprinting among US users [50.699390248359265]
Browser fingerprinting can be used to identify and track users across the Web, even without cookies.
This technique and resulting privacy risks have been studied for over a decade.
We provide a first-of-its-kind dataset to enable further research.
arXiv Detail & Related papers (2024-10-09T14:51:58Z) - To Be or Not to Be (in the EU): Measurement of Discrepancies Presented in Cookie Paywalls [0.0]
This study explores the effects of three factors: 1) the clients' browser, 2) the device type (desktop or mobile), and 3) the geographic location on the presence and behavior of cookie paywalls.
Using an automatic crawler on our dataset composed of 804 websites that present a cookie paywall, we observed that the presence of a cookie paywall was most affected by the geographic location of the user.
arXiv Detail & Related papers (2024-10-09T14:18:12Z) - A first look into Utiq: Next-generation cookies at the ISP level [3.434440572295625]
Third-party cookies have been widely used for years, they have also been criticized for their potential impact on user privacy.
Many browsers allow users to block third-party cookies, which limits their usefulness for advertisers.
We take a first look at Utiq, a new way of user tracking performed directly by the ISP, to substitute the third-party cookies.
arXiv Detail & Related papers (2024-05-15T09:23:59Z) - Measuring Strategization in Recommendation: Users Adapt Their Behavior to Shape Future Content [66.71102704873185]
We test for user strategization by conducting a lab experiment and survey.
We find strong evidence of strategization across outcome metrics, including participants' dwell time and use of "likes"
Our findings suggest that platforms cannot ignore the effect of their algorithms on user behavior.
arXiv Detail & Related papers (2024-05-09T07:36:08Z) - User Attitudes to Content Moderation in Web Search [49.1574468325115]
We examine the levels of support for different moderation practices applied to potentially misleading and/or potentially offensive content in web search.
We find that the most supported practice is informing users about potentially misleading or offensive content, and the least supported one is the complete removal of search results.
More conservative users and users with lower levels of trust in web search results are more likely to be against content moderation in web search.
arXiv Detail & Related papers (2023-10-05T10:57:15Z) - A Quantitative Information Flow Analysis of the Topics API [0.34952465649465553]
We analyze the re-identification risk for individual Internet users introduced by the Topics API from the perspective of information- and decision-theoretic framework.
Our model allows a theoretical analysis of both privacy and utility aspects of the API and their trade-off, and we show that the Topics API does have better privacy than third-party cookies.
arXiv Detail & Related papers (2023-09-26T08:14:37Z) - Protecting User Privacy in Online Settings via Supervised Learning [69.38374877559423]
We design an intelligent approach to online privacy protection that leverages supervised learning.
By detecting and blocking data collection that might infringe on a user's privacy, we can restore a degree of digital privacy to the user.
arXiv Detail & Related papers (2023-04-06T05:20:16Z) - User Tracking in the Post-cookie Era: How Websites Bypass GDPR Consent
to Track Users [3.936965297430477]
We investigate whether websites use persistent and sophisticated forms of tracking in order to track users who said they do not want cookies.
Our results suggest that websites do use such modern forms of tracking even before users had the opportunity to register their choice with respect to cookies.
As a result, users' choices play very little role with respect to tracking.
arXiv Detail & Related papers (2021-02-17T14:11:10Z) - Federated Learning of User Authentication Models [69.93965074814292]
We propose Federated User Authentication (FedUA), a framework for privacy-preserving training of machine learning models.
FedUA adopts federated learning framework to enable a group of users to jointly train a model without sharing the raw inputs.
We show our method is privacy-preserving, scalable with number of users, and allows new users to be added to training without changing the output layer.
arXiv Detail & Related papers (2020-07-09T08:04:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.