In this paper we investigate the question whether users' personalities are good predictors for privacy-related permissions they would grant to apps installed on their mobile devices. We report on results of a large online study (n=100) which reveals a significant correlation between the user's personality according to the big five personality scores, or the IUIPC questionnaire, and the app permission settings they have chosen. We used machine learning techniques to predict user privacy settings based on their personalities and consequently introduce a novel strategy that simplifies the process of granting permissions to apps.

Using virtual models of a real environment to improve performance and design effective and efficient user interfaces has always been a matter of choice to provide control of complex environments. The concept of Dual Reality has gone one step further in synchronizing a real environment with its virtualization. So far, little is known about the design of effective Dual Reality interfaces. With this paper we want to shed light on this topic by comparing the strategies, performance and efficiency in a real, virtualized and a DR setting given a complex task. We propose a cost and efficiency measure for complex tasks, and have conducted an experiment based on a complex shelf planning task. Our results show that for certain tasks interacting with the virtual world yields better results, whereas the best effectivity can be observed in a Dual Reality setup. We discuss these results and present design guidelines for future Dual Reality interfaces.

Intelligent retail stores like Amazon Go collect and process a large amount of shoppers' personal data to offer their service. In this paper we present Retailio, privacy management software that allows the customer to select the private data that should be accessible by retail stores. A privacy wizard helps the user to set her privacy settings, by using either a small informal privacy questionnaire or privacy measures extracted out of the user's Facebook posts for a machine learning-based prediction of user-tailored privacy settings. We conducted an expert interview to determine the different types of data that could be recorded in intelligent retail stores, and performed a user study to find out whether their disclosures correlate with shoppers' personalities. Retailio was evaluated in a validation study, regarding accuracy of the privacy wizard and user experience of the software. Our results show that there is a strong correlation between the IUIPC questionnaire and the data disclosure choice, which allowed us to predict the privacy settings with 70% accuracy.

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Amazon recently opened its first intelligent retail store, which captures shopper movements, picked-up products and much more sensitive data. In this paper we present a privacy UI, called URetail, that returns to the customer control over his own data, by offering an interface to select which of his private data items should be disclosed. We use a radar metaphor to arrange the permissions with ascending sensitivity into different clusters, and introduce a new multi-dimensional form of a radar interface called the privacy pyramid. We conducted an expert interview and a pilot study to determine which types of data are recorded in an intelligent retail store, and grouped them with ascending sensitivity into clusters. A preliminary evaluation study shows that radar interfaces have their own strengths and weaknesses compared to a conventional UI.