Mobile Data Transparency and Control

Mobile devices carry sensitive information that is routinely transmitted over the network and is often collected for analytics and targeted advertising purposes. Users are typically unaware of this data collection and have little control over their information. In this thesis, we develop systems and techniques that provide users with increased transparency and control over their mobile data. We develop AntMonitor - a user space app that can inspect and analyze all network traffic coming in and out of the device. AntMonitor outperforms prior art in terms of network throughput and CPU usage. Our tool enables real-time packet interception and analysis on the mobile device, including the following three applications. First, we build AntShield, which builds on top of AntMonitor to intercept packets, and uses a combination of deep packet inspection and machine learning to detect and block outgoing traffic that contains personally identifiable information. Second, we develop NoMoAds - the first mobile-specific, cross-app, machine learning-based ad-blocker, and we show that it outperforms state-of-the-art filter lists. Third, we build AutoLabel - a system for automatically labeling which packets were sent by apps and which by third-party advertisement or analytics libraries. This eliminates the need for manual labeling – which is the major bottleneck in creating filter lists and classifiers. Overall, this thesis follows a network-based approach to enhance mobile data transparency and give users control over their data