Predicting Privacy Preferences for Smart Devices as Norms
This addresses the issue of users providing automatic, inaccurate responses when directly asked for privacy preferences, which is a problem for smart device users and developers, though it appears incremental as it applies an existing method to a new domain.
The paper tackles the problem of accurately capturing user privacy preferences for smart devices by proposing a collaborative filtering approach to predict these preferences as norms, achieving test accuracy on a dataset of smart assistant users.
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.