Grant Hernandez

Joseph I. Choi, Dave 'Jing' Tian, Grant Hernandez et al.

A protocol for two-party secure function evaluation (2P-SFE) aims to allow the parties to learn the output of function $f$ of their private inputs, while leaking nothing more. In a sense, such a protocol realizes a trusted oracle that computes $f$ and returns the result to both parties. There have been tremendous strides in efficiency over the past ten years, yet 2P-SFE protocols remain impractical for most real-time, online computations, particularly on modestly provisioned devices. Intel's Software Guard Extensions (SGX) provides hardware-protected execution environments, called enclaves, that may be viewed as trusted computation oracles. While SGX provides native CPU speed for secure computation, previous side-channel and micro-architecture attacks have demonstrated how security guarantees of enclaves can be compromised. In this paper, we explore a balanced approach to 2P-SFE on SGX-enabled processors by constructing a protocol for evaluating $f$ relative to a partitioning of $f$. This approach alleviates the burden of trust on the enclave by allowing the protocol designer to choose which components should be evaluated within the enclave, and which via standard cryptographic techniques. We describe SGX-enabled SFE protocols (modeling the enclave as an oracle), and formalize the strongest-possible notion of 2P-SFE for our setting. We prove our protocol meets this notion when properly realized. We implement the protocol and apply it to two practical problems: privacy-preserving queries to a database, and a version of Dijkstra's algorithm for privacy-preserving navigation. Our evaluation shows that our SGX-enabled SFE scheme enjoys a 38x increase in performance over garbled-circuit-based SFE. Finally, we justify modeling of the enclave as an oracle by implementing protections against known side-channels.

Grant Hernandez, Farhaan Fowze, Dave Tian et al.

The USB protocol has become ubiquitous, supporting devices from high-powered computing devices to small embedded devices and control systems. USB's greatest feature, its openness and expandability, is also its weakness, and attacks such as BadUSB exploit the unconstrained functionality afforded to these devices as a vector for compromise. Fundamentally, it is virtually impossible to know whether a USB device is benign or malicious. This work introduces FirmUSB, a USB-specific firmware analysis framework that uses domain knowledge of the USB protocol to examine firmware images and determine the activity that they can produce. Embedded USB devices use microcontrollers that have not been well studied by the binary analysis community, and our work demonstrates how lifters into popular intermediate representations for analysis can be built, as well as the challenges of doing so. We develop targeting algorithms and use domain knowledge to speed up these processes by a factor of 7 compared to unconstrained fully symbolic execution. We also successfully find malicious activity in embedded 8051 firmwares without the use of source code. Finally, we provide insights into the challenges of symbolic analysis on embedded architectures and provide guidance on improving tools to better handle this important class of devices.