Next Thursday, we’re lucky to have Dave Choffnes visiting the lab. David Choffnes is an assistant professor in the College of Computer and Information Science at Northeastern University. His research is primarily in the areas of distributed systems and networking, with a recent focus on mobile systems and privacy. Much of his work entails crowdsourcing measurement and performance evaluation of Internet systems by deploying software to users at the scale of tens or hundreds of thousands of users. He earned his PhD from Northwestern (not in the northwest), and completed a postdoc at the University of Washington (in the northwest) prior to joining Northeastern (both in the northeast and northwest). He sees no reason why this should at all be confusing. He is a co-author of three textbooks, and his research has been supported by the NSF, Google, the Data Transparency Lab, VidScale, M-Lab, and a Computing Innovations Fellowship.
- Time: Thursday May 19th, 11am
- Location: DTU, Building 321, 1st floor lab space
Title: ReCon: Identifying and Controlling Privacy Leaks from Mobile Devices
Abstract: Mobile systems have become increasingly popular for providing ubiquitous Internet access; however, recent studies demonstrate that software running on these systems extensively tracks and leaks users’ personally identifiable information (PII). I argue that these privacy leaks persist in large part because mobile users have little visibility into PII leaked through the network traffic generated by their devices, and have poor control over how, when and where that traffic is sent and handled by third parties.
In this talk, I describe ReCon, a cross-platform system that reveals PII leaks and gives users control over them without requiring any special privileges or custom OSes. Specifically, our key observation is that PII leaks must occur over the network, so we implement our system in the network using a software middlebox. We then use a machine learning approach to to efficiently and accurately detect users’ PII without knowing a priori the content that is PII. Further, we develop techniques to block, obfuscate, or ignore the PII leak, by displaying leaks via a visualization tool and letting the user decide how the system should act on transmitted PII. I discuss the design and implementation of the system and evaluate its methodology with measurements from controlled experiments and flows from a user study with more than 100 participants. In addition to revealing and controlling PII leaks, we are using our machine-learning-based techniques to automatically identify and block malware based on network behaviors.