Enhancing User Privacy in Mobile Devices Through Prediction of Privacy Preferences

Abstract

The multitude of applications and security configurations of mobile devices requires automated approaches for effective user privacy protection. Current permission managers, the core mechanism for privacy protection in smartphones, have shown to be ineffective by failing to account for privacy’s contextual dependency and personal preferences within context. In this paper we focus on the relation between privacy decisions (e.g. grant or deny a permission request) and their surrounding context, through an analysis of a real world dataset obtained in campaigns with 93 users. We leverage such findings and the collected data to develop methods for automated, personalized and context-aware privacy protection, so as to predict users’ preferences with respect to permission requests. Our analysis reveals that while contextual features have some relevance in privacy decisions, the increase in prediction performance of using such features is minimal, since two features alone are capable of capturing a relevant effect of context changes, namely the category of the requesting application and the requested permission. Our methods for prediction of privacy preferences achieved an F1 score of 0.88, while reducing the number of privacy violations by 28% when compared to the standard Android permission manager.

This work is supported by project COP-MODE, that has received funding from the European Union’s Horizon 2020 research and innovation programme under the NGI TRUST grant agreement no 825618, and the project SNOB-5G with Nr. 045929 (CENTRO-01-0247-FEDER-045929) supported by the European Regional Development Fund (FEDER), through the Regional Operational Programme of Centre (CENTRO 2020) of the Portugal 2020 framework and FCT under the MIT Portugal Program. Ricardo Mendes and Mariana Cunha wish to acknowledge the Portuguese funding institution FCT - Foundation for Science and Technology for supporting their research under the Ph.D. grant SFRH/BD/128599/2017 and 2020.04714.BD, respectively.

Appendices

A A Grant Rate

Figure 6 presents the average grant result for each pair of category of the requesting app and requested permission.

Fig. 6.

Average grant result for each pair of category-permission. The number in each cell is the number of requests for the respective pair category-permission group, and GR is the grant rate for the respective category or permission. Categories and permissions with less than 10 requests were removed.

B B Information Gain

Table 2 presents the information gain for the grant result with each other feature in the dataset, where categorical features were one-hot encoded.

Table 2. Information Gain for the grant result with every other feature. Showing only values greater than 0.