Although privacy settings are important not only for data privacy, but also to prevent hacking attacks like social engineering that depend on leaked private data, most users do not care about them. Research has tried to help users in setting their privacy settings by using some settings that have already been adapted by the user or individual factors like personality to predict the remaining settings. But in some cases, neither is available. However, the user might have already done privacy settings in another domain, for example, she already adapted the privacy settings on the smartphone, but not on her social network account. In this article, we investigate with the example of four domains (social network posts, location sharing, smartphone app permission settings and data of an intelligent retail store), whether and how precise privacy settings of a domain can be predicted across domains. We performed an exploratory study to examine which privacy settings of the aforementioned domains could be useful, and validated our findings in a validation study. Our results indicate that such an approach works with a prediction precision about 15%–20% better than random and a prediction without input coefficients. We identified clusters of domains that allow model transfer between their members, and discuss which kind of privacy settings (general or context-based) leads to a better prediction accuracy. Based on the results, we would like to conduct user studies to find out whether the prediction precision is perceived by users as a significant improvement over a “one-size-fits-all” solution, where every user is given the same privacy settings.

Creating friend lists offers social network users the ability to select a fine-grained audience for their posts, thereby reducing the amount of unwanted disclosures. However, research has shown that the user burden involved in creating and managing friend lists leads to the fact that this functionality is rarely used, despite its advantages. In this paper, we propose two design concepts using virtual reality to allow the user to create and organize her friend lists. Whereas the first “pragmatic” concept is targeted towards usability and practicability using a metaphor similar to card sorting, the second “playful” concept has the goal to achieve a high user experience score by offering a VR game to sort and organize the friends. In a lab study, we compared the two concepts with the Facebook interface in terms of usability, user experience and error rate (like missing friends in a group or friends placed in the wrong group). We were able to show that both designs significantly outperform the Facebook interface in both usability and user experience. The playful interface is experienced as more interesting and stimulating than its pragmatic counterpart, at the cost of an increased error rate.

Selecting the right audience for Facebook posts is a task that users often skip, resulting in unwanted post disclosure or avoidance of sharing sensitive posts. We present OmniWedges, a user interface designed to allow users of online social networks to make meaningful decisions on who to share their posts with. Our study results also show that with all Facebook friends, the error rate can be significantly reduced compared to the Facebook interface. In an interview, we were also able to spot a change in posting behavior and frequency with our interface.

In social networks, it often arises that a post is shared with a broader audience than intended, which is often finally noticed when one of the unintentionally included friends likes or comments on the post. We present an approach for privacy setting adaptation based on in-situ feedback on such social network update notifications. We implemented a smartphone application that allows users to give positive or negative feedback using two buttons integrated into Facebook’s update notifications. We collected qualitative feedback from focus groups to find out what impact of in-situ feedback on privacy settings is expected by users. Our findings indicate that there is no general rule of thumb on how the privacy settings should be adapted. Nevertheless, the discussion led to a new approach that allows users to manage and adapt her privacy settings, and which is also capable of performing content elicitation and filtering for social network sites.

Research has observed context factors like occasion and time as influential factors for predicting whether or not to share a location with online friends. In other domains like social networks, personality was also found to play an important role. Furthermore, users are seeking a fine-grained disclosement policy that also allows them to display an obfuscated location, like the center of the current city, to some of their friends. In this paper, we observe which context factors and personality measures can be used to predict the correct privacy level out of seven privacy levels, which include obfuscation levels like center of the street or current city. Our results show that a prediction is possible with a precision 20% better than a constant value. We will give design indications to determine which context factors should be recorded, and how much the precision can be increased if personality and privacy measures are recorded using either a questionnaire or automated text analysis.

Current research has shown that a person's personality can be derived from written text on Facebook or Twitter, as well as the amount of information shared on their personal social network sites. So far, there has been no further investigation on whether a person's privacy measures can be extracted from these information sources. We conducted an explorative online user study with 100 participants; the results indicate that privacy concerns can be derived from written text, with a prediction precision similar to personality. At the end of the discussion, we give specific guidelines on the choice of the correct data source for the derivation of the different privacy measures and the possible applications of those.

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.

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.

This paper proposes to recommend privacy settings to users of social networks (SNs) depending on the topic of the post. Based on the answers to a specifically designed questionnaire, machine learning is utilized to inform a user privacy model. The model then provides, for each post, an individual recommendation to which groups of other SN users the post in question should be disclosed. We conducted a pre-study to find out which friend groups typically exist and which topics are discussed. We explain the concept of the machine learning approach, and demonstrate in a validation study that the generated privacy recommendations are precise and perceived as highly plausible by SN users.

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.

Im Projekt APPsist wird eine Architektur für Assistenzund Wissensdienste zur Unterstützung der Beschäftigten in der Industrie 4.0 entwickelt, bestehend aus Basisdiensten und intelligent-adaptiven Diensten, die angepasst an den Kontext und den Benutzer die Unterstützung realisieren. Das Projekt befindet sich nahe dem Ende der Laufzeit, und dieser Beitrag beschreibt das finale System und gibt eine kritische Betrachtung der zu Projektbeginn getroffenen technischen Entscheidungen.

We present Privacy Wedges, a user interface designed to allow users of online social networks to make meaningful decisions on who to share their posts with. By displaying the privacy settings for historical posts, it is possible to visualize them in a meaningful and comprehensive way. We conducted a user study with 26 participants that showed that unwanted disclosure could be signicantly reduced compared to the current implementation of Facebook. That is, there were signicantly fewer posts shown to friends they were not appropriate for or intended for.

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.

Precision tasks in 3D like object manipulation or character animation call for new gestural interfaces that utilize many input degrees of freedom. We present MotionBender, a sensor-based interaction technique for post-editing the motion of e. g. the hands in character animation data. For the visualization of motion we use motion paths, often used for showing e.g. the movement of the hand through space over time, and allow the user to directly "bend" the 3D motion path with his/her hands and twist it into the right shape. In a comparative evaluation with a mouse-based interface we found that subjects using our technique were significantly faster. Moreover, with our technique, subject movement was more coordinated, i.e. movement was done in all three dimensions in parallel, and the participants preferred our technique in a post-experiment questionnaire. We also found a gender effect: male users both like the gesture interaction better and achieve better performance.