Automating privacy decisions -- where to draw the line?
Not on the map. Why?
Abstract (arXiv)
Users are often overwhelmed by privacy decisions to manage their personal data, which can happen on the web, in mobile, and in IoT environments. These decisions can take various forms -- such as decisions for setting privacy permissions or privacy preferences, decisions responding to consent requests, or to intervene and ``reject'' processing of one's personal data --, and each can have different legal impacts. In all cases and for all types of decisions, scholars and industry have been proposing tools to better automate the process of privacy decisions at different levels, in order to enhance usability. We provide in this paper an overview of the main challenges raised by the automation of privacy decisions, together with a classification scheme of the existing and envisioned work and proposals addressing automation of privacy decisions.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 7 of its 67 references that are in the map. First posted on arXiv 15 May 2023.
Builds on
7 of 67 references.
References · 7
- 2016Membership Inference Attacks against Machine Learning Modelscited by 2,221
- 2020Dark Patterns after the GDPR: Scraping Consent Pop-ups and Demonstrating their Influencecited by 27
- 2017The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferencescited by 9
- 2022AI, Opacity, and Personal Autonomycited by 4
- 2019Analysis of Privacy Policies to Enhance Informed Consent (Extended Version)cited by 2
- 2020Persuasion Meets AI: Ethical Considerations for the Design of Social Engineering Countermeasurescited by 2
- 2021Privacy Preference Signals: Past, Present and Futurecited by 2
Built on by
Citing papers per year: 2025: 1.
1 paper.