Tools for Achieving Usable Ex Post Transparency: A Survey <s> VOLUME 5, 2017 <s> Studies have shown that users have great difficulty specifying their security and privacy policies in a variety of application domains. While machine learning techniques have successfully been used to refine models of user preferences, such as in recommender systems, they are generally configured as "black boxes" that take control over the entire policy and severely restrict the ways in which the user can manipulate it. This article presents an alternative approach, referred to as user-controllable policy learning. It involves the incremental manipulation of policies in a context where system and user refine a common policy model. The user regularly provides feedback on decisions made based on the current policy. This feedback is used to identify (learn) incremental policy improvements which are presented as suggestions to the user. The user, in turn, can review these suggestions and decide which, if any, to accept. The incremental nature of the suggestions enhances usability, and because the user and the system manipulate a common policy representation, the user retains control and can still make policy modifications by hand. Results obtained using a neighborhood search implementation of this approach are presented in the context of data derived from the deployment of a friend finder application, where users can share their locations with others, subject to privacy policies they refine over time. We present results showing policy accuracy, which averages 60% upon initial definition by our users climbing as high as 90% using our technique. <s> BIB001 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> VOLUME 5, 2017 <s> A number of mobile applications have emerged that allow users to locate one another. However, people have expressed concerns about the privacy implications associated with this class of software, suggesting that broad adoption may only happen to the extent that these concerns are adequately addressed. In this article, we report on our work on PeopleFinder, an application that enables cell phone and laptop users to selectively share their locations with others (e.g. friends, family, and colleagues). The objective of our work has been to better understand people's attitudes and behaviors towards privacy as they interact with such an application, and to explore technologies that empower users to more effectively and efficiently specify their privacy preferences (or "policies"). These technologies include user interfaces for specifying rules and auditing disclosures, as well as machine learning techniques to refine user policies based on their feedback. We present evaluations of these technologies in the context of one laboratory study and three field studies. <s> BIB002 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> VOLUME 5, 2017 <s> Personal and contextual information are increasingly shared via mobile social networks. Users' locations, activities and their co-presence can be shared easily with online "friends", as their smartphones already access such information from embedded sensors and storage. Yet, people usually exhibit selective sharing behavior depending on contextual attributes, thus showing that privacy, utility, and usability are paramount to the success of such online services. In this paper, we present SPISM, a novel information-sharing system that decides (semi-)automatically whether to share information with others, whenever they request it, and at what granularity. Based on active machine learning and context, SPISM adapts to each user's behavior and it predicts the level of detail for each sharing decision, without revealing any personal information to a third-party. Based on a personalized survey about information sharing involving 70 participants, our results provide insight into the most influential features behind a sharing decision. Moreover, we investigate the reasons for the users' decisions and their confidence in them. We show that SPISM outperforms other kinds of global and individual policies, by achieving up to 90% of correct decisions. <s> BIB003 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> VOLUME 5, 2017 <s> Location-Based Mobile Service (LBMS) is one of the most popular smartphone services. LBMS enables people to more easily connect with each other and analyze the aspects of their lives. However, sharing location data can leak people's privacy. We present PDVLoc, a controlled location data-sharing framework based on selectively sharing data through a Personal Data Vault (PDV). A PDV is a privacy architecture in which individuals retain ownership of their data. Data are routinely filtered before being shared with content-service providers, and users or data custodian services can participate in making controlled data-sharing decisions. Introducing PDVLoc gives users flexible and granular access control over their location data. We have implemented a prototype of PDVLoc and evaluated it using real location-sharing social networking applications, Google Latitude and Foursquare. Our user study of 19 participants over 20 days shows that most users find that PDVLoc is useful to manage and control their location data, and are willing to continue using PDVLoc. <s> BIB004
The TET presented by Kelley at al. BIB001 is built on the concept of 'user-controllable policy learning,' a cooperative approach between the user and the policy management system. Based on the user's settings, the system makes automated decisions in lieu of an originator as regards disclosing or not disclosing that user's data to querying third parties. By reviewing and commenting on the choices made by the system, as well as by making changes to their settings, users help to train future decisions of the system, ultimately optimising the user's settings by applying incremental updates. Similarly, the TET presented by Mun et al. BIB004 is based on an architecture that implements a 'personal data vault.' The TET provides users with an active 'rule recommender' that suggests changes to the users' privacy settings. The tool visualises possible risks based on the user's settings, thus helping her find her optimal settings that best support utility and privacy. The recommender system of the TET presented by Sadeh et al. BIB002 specifically aids users in refining their privacy settings by providing them with meaningful suggestions for changes. Conversely, 'SPISM,' the TET presented by Bilogrevic et al. BIB003 , assessed the nominal values as regards a user's sharing behaviour by means of an extensive online survey, which the authors use to measure the accuracy of the automated decision-making of their TET.