# Predicting Privacy Preferences for Smart Devices as Norms

arXiv: https://arxiv.org/abs/2302.10650 · HTML: https://mlsanity.com/paper/2302.10650/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 32 references that are in the map.
- First posted on arXiv 21 Feb 2023.

## Builds on (most cited first, 2 of 2)

- [Value alignment: a formal approach](https://mlsanity.com/paper/2110.09240/) (2021)
- [Justifying Social-Choice Mechanism Outcome for Improving Participant Satisfaction](https://mlsanity.com/paper/2205.15863/) (2022)

## Built on by (most cited first, 2 of 2)

- [Towards Automating Data Access Permissions in AI Agents](https://mlsanity.com/paper/2511.17959/) (2025)
- [AI-driven Personalized Privacy Assistants: a Systematic Literature Review](https://mlsanity.com/paper/2502.07693/) (2025)

## Abstract (arXiv)

Smart devices, such as smart speakers, are becoming ubiquitous, and users expect these devices to act in accordance with their preferences. In particular, since these devices gather and manage personal data, users expect them to adhere to their privacy preferences. However, the current approach of gathering these preferences consists in asking the users directly, which usually triggers automatic responses failing to capture their true preferences. In response, in this paper we present a collaborative filtering approach to predict user preferences as norms. These preference predictions can be readily adopted or can serve to assist users in determining their own preferences. Using a dataset of privacy preferences of smart assistant users, we test the accuracy of our predictions.