Smartphone owners often need to run security-critical programs on the same device as other untrusted and potentially malicious programs. This requires users to trust hardware and system software to correctly sandbox malicious programs, trust that is often misplaced. Our goal is to minimize the number and complexity of hardware and software components that a smartphone owner needs to trust. We present a split-trust hardware design composed of statically-partitioned, physically-isolated trust domains. We introduce a few simple, formally-verified hardware components to enable a program to gain provably exclusive and simultaneous access to both computation and I/O on a temporary basis. To manage this hardware, we present OctopOS, an OS composed of mutually distrustful subsystems. We present a prototype of this machine (hardware and OS) on a CPU-FPGA board and show that it incurs a small hardware cost compared to modern smartphone SoCs. For security-critical programs, we show that this machine significantly reduces the required trust compared to mainstream TEEs while achieving usable performance. For normal programs, performance is similar to a legacy machine.

Voice assistants raise serious security and privacy concerns because they use always-on microphones in sensitive locations (e.g., inside a home) and send audio recordings to the cloud for processing. The cloud transcribes these recordings and interprets them as user requests, and sometimes even shares these requests with third-party services. These steps may result in unintended or malicious voice data leaks and in unauthorized actions, such as a purchase. This paper presents MegaMind, a novel extensible platform that lets a user deploy security and privacy extensions locally on their voice assistant. MegaMind's extensions interpose on requests before sending them to the cloud and on responses before delivering them to the user. MegaMind's programming model enables writing powerful extensions with ease, such as one for secure conversations. Additionally, MegaMind protects against malicious extensions by providing two important guarantees, namely permission enforcement and non-interference. We implement MegaMind and integrate it with Amazon Alexa Service SDK. Our evaluation shows that MegaMind achieves a small conversation latency on platforms with adequate compute power, such as a Raspberry Pi 4 and an x86-based laptop.

Spin transfer torque (STT-RAM) is a fast, scalable and non-volatile memory technology. These characteristics make STT-RAM one of the best candidates among memories that can be used for space applications. Although STT-RAM cell itself is immune to high energy particles, its sensing circuit might be severely affected by radiation. In this work, we first extensively analyze the effect of radiation on the STT-RAM sense circuit and then propose a radiation hardened circuit. Using a dual modular redundancy and a voting scheme, radiation susceptibility of the sense circuit is eliminated. The sense circuit is implemented in 45 nm CMOS technology. Simulation results show that the proposed circuit is immune to radiation pulses up to 400 Krad.