Biblio

A localized electromagnetic (EM) attack is a potent threat to security of embedded cryptographic implementations. The attack utilizes high resolution EM probes to localize and exploit information leakage in sub-circuits of a system, providing information not available in traditional EM and power attacks. In this paper, we propose a countermeasure based on randomizing the assignment of sensitive data to parallel datapath components in a high-performance implementation of AES. In contrast to a conventional design where each state register byte is routed to a fixed S-box, a permutation network, controlled by a transient random value, creates a dynamic random mapping between the state registers and the set of S-boxes. This randomization results in a significant reduction of exploitable leakage.We demonstrate the countermeasure's effectiveness under two attack scenarios: a more powerful attack that assumes a fully controlled access to an attacked implementation for building a priori EM-profiles, and a generic attack based on the black-box model. Spatial randomization leads to a 150× increase of the minimum traces to disclosure (MTD) for the profiled attack and a 3.25× increase of MTD for the black-box model attack.

We present a technique for implementing dataflow networks as compositional hardware circuits. We first define an abstract dataflow model with unbounded buffers that supports data-dependent blocks (mux, demux, and nondeterministic merge); we then show how to faithfully implement such networks with bounded buffers and handshaking. Handshaking admits compositionality: our circuits can be connected with or without buffers and still compute the same function without introducing spurious combinational cycles. As such, inserting or removing buffers affects the performance but not the functionality of our networks, which we demonstrate through experiments that show how design space can be explored.

In this paper, we address the design an implementation of low power embedded systems for real-time tracking of humans and vehicles. Such systems are important in applications such as activity monitoring and border security. We motivate the utility of mobile devices in prototyping the targeted class of tracking systems, and demonstrate a dataflow-based and cross-platform design methodology that enables efficient experimentation with key aspects of our tracking system design, including real-time operation, experimentation with advanced sensors, and streamlined management of design versions on host and mobile platforms. Our experiments demonstrate the utility of our mobile-device-targeted design methodology in validating tracking algorithm operation; evaluating real-time performance, energy efficiency, and accuracy of tracking system execution; and quantifying trade-offs involving use of advanced sensors, which offer improved sensing accuracy at the expense of increased cost and weight. Additionally, through application of a novel, cross-platform, model-based design approach, our design requires no change in source code when migrating from an initial, host-computer-based functional reference to a fully-functional implementation on the targeted mobile device.