UC Santa Barbara

Privacy in the digital age has become increasingly difficult to achieve. While there is consensus on the importance of building privacy into systems that deal with sensitive information, our ability to reason about system-level privacy is severely limited. In this work, I introduce wringing, a new computer architecture approach for building privacy in systems to minimize information leakage. I detail how wringing enhances the privacy of program traces and how it opens up a new optimization space between privacy and utility.

Next, I demonstrate how wringing generalizes beyond traces: in computer vision pipelines that rely on streaming user data for localization tasks in augmented reality settings. We discover a new reverse engineering attack on localization pipelines that can compromise user privacy and show that data minimizing wringing serves as a mitigation for such attacks.

Finally, I present a new architecture that builds privacy into personal devices. Our architecture supports both data minimizing techniques like wringing and differential privacy to protect streaming data being crowd-sourced by a central aggregator. With this hardware implementation, we can enforce the user's privacy settings and prevent unintended data leakage.