Default Privacy Setting Prediction by Grouping User’s Attributes and Settings Preferences

Abstract

While user-centric privacy settings are important to protect the privacy of users, often users have difficulty changing the default ones. This is partly due to lack of awareness and partly attributed to the tediousness and complexities involved in understanding and changing privacy settings. In previous works, we proposed a mechanism for helping users set their default privacy settings at the time of registration to Internet services, by providing personalised privacy-by-default settings. This paper evolves and evaluates our privacy setting prediction engine, by taking into consideration users’ settings preferences and personal attributes (e.g. gender, age, and type of mobile phone). Results show that while models built on users’ privacy preferences have improved the accuracy of our scheme; grouping users by attributes does not make an impact in the accuracy. As a result, services potentially using our prediction engine, could minimize the collection of user attributes and based the prediction only on users’ privacy preferences.