Tools for Achieving Usable Ex Post Transparency: A Survey <s> 1) LOGGING <s> The ever increasing popularity of apps stems from their ability to provide highly customized services to the user. The flip side is that in order to provide such services, apps need access to very sensitive private information about the user. This leads to malicious apps that collect personal user information in the background and exploit it in various ways. Studies have shown that current app vetting processes which are mainly restricted to install time verification mechanisms are incapable of detecting and preventing such attacks. We argue that the missing fundamental aspect here is a comprehensive and usable mobile privacy solution, one that not only protects the user's location information, but also other equally sensitive user data such as the user's contacts and documents. A solution that is usable by the average user who does not understand or care about the low level technical details. To bridge this gap, we propose privacy metrics that quantify low-level app accesses in terms of privacy impact and transforms them to high-level user understandable ratings. We also provide the design and architecture of our Privacy Panel app that represents the computed ratings in a graphical user-friendly format and allows the user to define policies based on them. Finally, experimental results are given to validate the scalability of the proposed solution. <s> BIB001 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> 1) LOGGING <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> BIB002 </s> Tools for Achieving Usable Ex Post Transparency: A Survey <s> 1) LOGGING <s> Mobile devices have revolutionized many aspects of our lives. We use smartphones and tablets as portable computers and, often without realizing it, we run various types of security-sensitive programs on them, such as personal and enterprise email and instant-messaging applications, as well as social, banking, insurance and retail programs. These applications access and transmit over the network numerous pieces of private information, including our geographical location, device ID, contacts, calendar events, passwords, and health records, as well as credit-card, social-security, and bank-account numbers. Guaranteeing that no private information is exposed to unauthorized observers is very challenging given the level of complexity that these applications have reached. Furthermore, using program-analysis tools with out-of-the-box configurations in order to detect confidentiality violations may not yield the desired results because only a few pieces of private data, such as the device's ID and geographical location, are obtained from standard sources. The majority of confidentiality sources (such as credit-card and bank-account numbers) are application-specific and require careful configuration. This paper presents Labyrinth, a run-time privacy enforcement system that automatically detects leakage of private data originating from standard as well as application-specific sources. Labyrinth features several novel contributions: (i) it allows for visually configuring, directly atop the application's User Interface (UI), the fields that constitute custom sources of private data, (ii) it does not require operating-system instrumentation, but relies only an application-level instrumentation and on a proxy that intercepts the communication between the mobile device and the back-end servers, and (iii) it performs an enhanced form of value-similarity analysis to detect data leakage even when sensitive data (such as a password) has been encoded or hashed. Labyrinth supports both Android and iOS. We have evaluated Labyrinth experimentally, and in this paper we report results on production-level applications. <s> BIB003
The transaction logging 11 locality refers to the process that scans and collects a data subject's personal data while she is interacting with a data processor. Once disclosed personal data are being detected, the incident is logged in the form of a transaction record for later review. TETs that make use of transaction logging offer the advantage of collecting additional factual data about the circumstances under which the user's personal data are being disclosed. Such information can be used later during the review process to verify the completeness of data retrieved from a data processor. However, transaction logging comes at the price of complicating the TET by monitoring local data processing. Detecting disclosed personal data in data streams requires a significant amount of control over the respective hosting platform, and may require supervisor privileges or extended permissions in order to scrutinise outbound data traffic  ,  , BIB001 , BIB002 , BIB003 .