Easing the Burden of Setting Privacy Preferences: A Machine Learning Approach

Abstract

Setting appropriate privacy preferences is both a difficult and cumbersome task for users. In this paper, we propose a solution to address users’ privacy concerns by easing the burden of manually configuring appropriate privacy settings at the time of their registration into a new system or service. To achieve this, we implemented a machine learning approach that provides users personalized privacy-by-default settings. In particular, the proposed approach combines prediction and clustering techniques, for modeling and guessing the privacy profiles associated to users’ privacy preferences. This approach takes into consideration the combinations of service providers, types of personal data and usage purposes. Based on a minimal number of questions that users answer at the registration phase, it predicts their privacy preferences and sets an optimal default privacy setting. We evaluated our approach with a data set resulting from a questionnaire administered to 10,000 participants. Results show that with a limited user input of 5 answers the system is able to predict the personalised privacy settings with an accuracy of 85%.