Tools for Achieving Usable Ex Post Transparency: A Survey <s> d: PREDICTED DATA <s> Information sharing on the Internet has become a streamlined process through services such as e-mail, blogs and recently also Online Social Networks (OSNs). The speed of posting information in one central place and the convenience of this information reaching a vast audience at the same time is a great advantage of this style of communication, but at the same time also a danger. The aim of this article is thus to introduce a prototype software model that can analyse not only the Personally Identifiable Information (PII) that users share on OSNs, but with whom they share this. Such analysis will give users a good indication of how their private information is shared based on different settings within their OSN profiles. To successfully develop such a model, familiarity with the concepts of information sharing and PII is firstly investigated. Identifying information leakage within one of the most popular OSNs follows. In combination with our programming knowledge, a prototype model is introduced and an overview of preliminary results is given. Due to the generic nature of the model, we are able to visually introduce and explain the process of information deduction -- an aspect of information sharing often overlooked due to its invisible nature. After analysing the information gathered from our model, we conclude that the immense popularity of Facebook on both a global and local scale contribute to its success, but at the same time make its users more prone to leak information and subsequently be the potential targets of cyber criminals. The ease, anonymity and borderless nature of the Internet in combination with the poor default security settings of our investigated OSN all contribute to the lucrative industry of cyber crime. Proper user awareness is crucial to preventing the leaking of any information in an online environment, but for many users this still equates to (a) PII in the sky. <s> BIB001 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> d: PREDICTED DATA <s> The ubiquitous availability of location data to smartphone apps and online social networks has caused the collection of such information to grow at an unprecedented rate. However, the discriminative power and potential uses of this data collection is not always clear to the end user. In this work, we present FindYou, a web-based application that gives users the ability to perform a location data privacy audit. FindYou lets users import and visualize the location data collected by popular web services in order to understand what these companies know or can easily infer about them. Additionally, FindYou gives users the option to donate their data to the scientific community, creating new mobile datasets linked to user properties that will be open to use by academic institutions. We hope that FindYou will increase awareness of the privacy issues surrounding the collection and use of location data, the potential problem of "digital red-lining", and also create valuable new datasets with the full informed consent of interested users. <s> BIB002
Predicted data refer to conclusions that might be drawn by a TET based on a set of data disclosed by the data subject.  They represent statements that may be made by an entity that has access to the respective set of data. Predicted data differ from derived data in that the latter are data produced by a data processor and transferred back to the data subject, whereas the former represent predictions made by the TET. Such predictions may or may not be accurate, and they may or may not be congruent with the conclusions drawn by a data processor. The TET presented by Riederer et al. BIB002 makes predictions about hypothetical facts based on actual personal data provided by the user. Once imported, 'FindYou' predicts statements regarding the ethnography of the data subject. This kind of predication is possible by combining the personal data with public data available through the United States Census database. Louw et al. BIB001 discuss the possibility to deduct certain personal traits of an individual or group of individuals even though the respective trait was not explicitly disclosed earlier. They argue, for example, that a person's gender can be deducted from her photograph or her name, while a description of activities and events may lead to the participants' location being inferred.