| Title: |
A User-Centered Privacy Policy Management System for Automatic Consent on Cookie Banners |
| Authors: |
Lorenzo Porcelli, Michele Mastroianni, Massimo Ficco, Francesco Palmieri |
| Source: |
Computers, Vol 13, Iss 2, p 43 (2024) |
| Publisher Information: |
MDPI AG, 2024. |
| Publication Year: |
2024 |
| Collection: |
LCC:Electronic computers. Computer science |
| Subject Terms: |
Personal Information Management System (PIMS), User Privacy Policy Management System (UPPMS), privacy paradox, Large Language Model (LLM), Generative Pre-trained Transformer (GPT), cookie banner, Electronic computers. Computer science, QA75.5-76.95 |
| More Details: |
Despite growing concerns about privacy and an evolution in laws protecting users’ rights, there remains a gap between how industries manage data and how users can express their preferences. This imbalance often favors industries, forcing users to repeatedly define their privacy preferences each time they access a new website. This process contributes to the privacy paradox. We propose a user support tool named the User Privacy Preference Management System (UPPMS) that eliminates the need for users to handle intricate banners or deceptive patterns. We have set up a process to guide even a non-expert user in creating a standardized personal privacy policy, which is automatically applied to every visited website by interacting with cookie banners. The process of generating actions to apply the user’s policy leverages customized Large Language Models. Experiments demonstrate the feasibility of analyzing HTML code to understand and automatically interact with cookie banners, even implementing complex policies. Our proposal aims to address the privacy paradox related to cookie banners by reducing information overload and decision fatigue for users. It also simplifies user navigation by eliminating the need to repeatedly declare preferences in intricate cookie banners on every visited website, while protecting users from deceptive patterns. |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
2073-431X |
| Relation: |
https://www.mdpi.com/2073-431X/13/2/43; https://doaj.org/toc/2073-431X |
| DOI: |
10.3390/computers13020043 |
| Access URL: |
https://doaj.org/article/cbf8445809914d868780584374d122b6 |
| Accession Number: |
edsdoj.bf8445809914d868780584374d122b6 |
| Database: |
Directory of Open Access Journals |
