Computing is undergoing a significant shift. First, the explosive growth of the Internet of Things (IoT) enables users to interact with computing systems and physical environments in novel ways through perceptual interfaces (e.g., microphones and cameras). Second, machine learning algorithms collect huge amounts of data and make critical decisions on new computing systems. While these trends bring unprecedented functionality, they also drastically increase the number of untrusted algorithms, implementations, interfaces, and the amount of private data processed by them, endangering user security and privacy. To regulate these security and privacy issues, privacy regulations such as GDPR (General Data Protection Regulation), CCPA (California Consumer Privacy Act) went into effect. However, there is a huge gap between the desired high-level security/privacy/ethical properties (from regulations, specifications, users’ expectations) and low-level real implementations. 

To bridge the gap, my work aims to change how platform architects design secure systems, assist developers by detecting security and privacy violation of implementations and build usable and scalable privacy-preserving systems. In this talk, I will present how my group designs principled solutions to ensure modern and emerging computing platforms' security and privacy. In this talk, I will introduce two developer tools we build to detect security and privacy violations. Using the tools, we found large numbers of policy violations in healthcare voice applications and security property violations in IoT messaging protocol implementations. Additionally, I will discuss our recent work on scalable privacy-preserving machine learning.