Social media platforms pose privacy risks when data is used in unexpected ways (e.g., for advertising or data sharing with partners). Using a custom browser extension and an online survey with 195 Facebook users, we investigated (1) whether participants’ expected values of their Facebook privacy settings were (mis)aligned with their actual settings; (2) demographic differences in privacy expectation-setting mismatches; and (3) participants' privacy concerns and trust towards Facebook.Our study presents a current and comprehensive analysis of Facebook users' privacy settings. We find that expectation-setting mismatches are prevalent: all participants had at least one mismatch; many had multiple, often expecting their settings to be more restrictive than they were. We also found that Facebook's default values are not aligned with people's expectations and/or actual settings, which suggests that those defaults are ineffective. Furthermore, mismatches differed along certain demographic variables.Participants' trust in Facebook decreased after they became aware of mismatches and their actual settings. Our empirical findings indicate that, despite increased public awareness, media scrutiny, and regulatory attention regarding privacy issues, there is still a substantial and concerning disconnect between how private people perceive their social media data to be and how exposed their data actually is, opening them up to both interpersonal and institutional privacy risks. We discuss design and public policy implications of our findings.

Floating car data (FCD), i.e. traffic and in-car data collected and transmitted by moving cars, is an emerging telematics application providing a range of networked services to road users. FCD is an example of a large sensor network, whose complexity increases with the number of session participants. Our work examines how deploying application-specific functions in the network can help in scaling such systems. We have chosen to configure these functions on a dedicated device that is commonly called a middlebox. These middleboxes use programmable network processors in order to attain the required processing and forwarding speeds, while communicating with each other and a back-end server using standard middleware components. We present our experience in using such a programmable middlebox in the scaling of a large telematics service, explaining how the middlebox fits into the end-to-end application.

Nowadays, the majority of productivity applications are interactive and graphical in nature. In this demonstration, we explore the possibility of taking advantage of these two characteristics in a design recovery tool. Specifically, the fact that an application is interactive means that we can identify distinct execution bursts corresponding closely to "actions" performed by the user. The fact that the application is graphical means that we can describe those actions visually from a fragment of the application display itself. Combining these two ideas, we obtain an explicit mapping from high-level actions performed by a user (similar to use case scenarios/specification fragments) to their low-level implementation. This mapping can be used for design recovery of interactive graphical applications. We demonstrate our approach using L/sub Y/X, a scientific word processor.

CVSSearch is a tool that searches for fragments of source code by using CVS comments. CVS is a version control system that is widely used in the open source community. Our search tool takes advantage of the fact that a CVS comment typically describes the lines of code involved in the commit and this description will typically hold for many future versions. In other words, CVSSearch allows one to better search the most recent version of the code by looking at previous versions to better understand the current version. In this paper we describe our algorithm for mapping CVS comments to the corresponding source code, present a search tool based on this technique, and discuss preliminary feedback.