Different layers of the computer system, from the low-level hardware accelerators and networks-on-chip (NoC) in multi-core systems, to the upper-level operating systems and software applications, rely on the sharing of hardware computing resources. Unfortunately such sharing, when not carefully managed, can introduce a host of protection problems and sources of information leakage. We describe a set of methods by which it is possible to systematically scale performance via hardware sharing without exacerbating security properties by being aware of the design and characteristics of individual layers and components. The key to this is efficiently dealing with security vulnerabilities introduced by sharing in terms of time and space through the creation of new security-conscious sharing interfaces. In a systematic way is to first define coordination techniques into more detailed patterns, and by bridging the gap of less efficient universal measures with provably more performant and secure patterns.

Specifically we demonstrate the usefulness of a sharing pattern for hardware and software systems where separation is of concern (interference and timing channel mitigation, etc). The most important insight is that in order to fully utilize computing resources (to improve performance and availability), the entities that share these resources must coordinate in a pre-calculated way. More dynamic approaches to improve performance and concurrency are likely to introduce new interference in the system. While we show that certain static scheduling measures in lower level hardware such as networks-on-chip can provably eliminate timing channels, the dynamic nature of software systems makes covert channels harder to be confined. Besides, software systems also face other types of security problems beyond side channels. To improve concurrency and performance without exacerbating security requires a slightly different approach.

To study the obstacles that hinder software applications' scaling in a system because of security concerns, we delve into the Android operating system and its appification ecosystem structure. A prime avenue for attack is introduced because of its distributed sharing eco-pattern. We propose a centralized approach with a single reliable service as a method to enable computation reuse among applications. The proposed centralization technique favors well-protected application-to-system communications over vulnerable application-to-application communications. Thus not only computation concurrency is boosted but also the possibility of an app being attacked through the attack-prone Inter-Component Calls (ICCs) due to possible distributed computation sharing is eliminated. This approach further enables improvements to security with the addition of a novel application-centric grouping for isolation. We show through a prototype on Android how our approach supports and protects inter-app resource sharing, while improving concurrency at scale.

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.