# The Feasibility of Dynamically Granted Permissions: Aligning Mobile Privacy with User Preferences

arXiv: https://arxiv.org/abs/1703.02090 · HTML: https://mlsanity.com/paper/1703.02090/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 Mar 2017.

## Built on by (most cited first, 9 of 9)

- [Privacy Leakage Overshadowed by Views of AI: A Study on Human Oversight of Privacy in Language Model Agent](https://mlsanity.com/paper/2411.01344/) (2024)
- [Digital Voodoo Dolls](https://mlsanity.com/paper/2105.02738/) (2021)
- [Can LLMs Make (Personalized) Access Control Decisions?](https://mlsanity.com/paper/2511.20284/) (2025)
- [Personalizing Agent Privacy Decisions via Logical Entailment](https://mlsanity.com/paper/2512.05065/) (2025)
- [Automating privacy decisions -- where to draw the line?](https://mlsanity.com/paper/2305.08747/) (2023)
- [User Perceptions vs. Proxy LLM Judges: Privacy and Helpfulness in LLM Responses to Privacy-Sensitive Scenarios](https://mlsanity.com/paper/2510.20721/) (2025)
- [MiniScope: A Least Privilege Framework for Authorizing Tool Calling Agents](https://mlsanity.com/paper/2512.11147/) (2025)
- [Guarding Digital Privacy: Exploring User Profiling and Security Enhancements](https://mlsanity.com/paper/2504.07107/) (2025)
- [PrivacySIM: Evaluating LLM Simulation of User Privacy Behavior](https://mlsanity.com/paper/2605.12147/) (2026)

## Abstract (arXiv)

Current smartphone operating systems regulate application permissions by prompting users on an ask-on-first-use basis. Prior research has shown that this method is ineffective because it fails to account for context: the circumstances under which an application first requests access to data may be vastly different than the circumstances under which it subsequently requests access. We performed a longitudinal 131-person field study to analyze the contextuality behind user privacy decisions to regulate access to sensitive resources. We built a classifier to make privacy decisions on the user's behalf by detecting when context has changed and, when necessary, inferring privacy preferences based on the user's past decisions and behavior. Our goal is to automatically grant appropriate resource requests without further user intervention, deny inappropriate requests, and only prompt the user when the system is uncertain of the user's preferences. We show that our approach can accurately predict users' privacy decisions 96.8% of the time, which is a four-fold reduction in error rate compared to current systems.