Implementing a protected zone in a reconfigurable processor for isolated execution of cryptographic algorithms

We design and realize a protected zone inside a reconfigurable and extensible embedded RISC processor for isolated execution of cryptographic algorithms. The protected zone is a collection of processor subsystems such as functional units optimized for high-speed execution of integer operations, a small amount of local memory, and general and special-purpose registers. We outline the principles for secure software implementation of cryptographic algorithms in a processor equipped with the protected zone. We also demonstrate the efficiency and effectiveness of the protected zone by implementing major cryptographic algorithms, namely RSA, elliptic curve cryptography, and AES in the protected zone. In terms of time efficiency, software implementations of these three cryptographic algorithms outperform equivalent software implementations on similar processors reported in the literature. The protected zone is designed in such a modular fashion that it can easily be integrated into any RISC processor; its area overhead is considerably moderate in the sense that it can be used in vast majority of embedded processors. The protected zone can also provide the necessary support to implement TPM functionality within the boundary of a processor

InternalBlue - Bluetooth Binary Patching and Experimentation Framework

Bluetooth is one of the most established technologies for short range digital wireless data transmission. With the advent of wearables and the Internet of Things (IoT), Bluetooth has again gained importance, which makes security research and protocol optimizations imperative. Surprisingly, there is a lack of openly available tools and experimental platforms to scrutinize Bluetooth. In particular, system aspects and close to hardware protocol layers are mostly uncovered. We reverse engineer multiple Broadcom Bluetooth chipsets that are widespread in off-the-shelf devices. Thus, we offer deep insights into the internal architecture of a popular commercial family of Bluetooth controllers used in smartphones, wearables, and IoT platforms. Reverse engineered functions can then be altered with our InternalBlue Python framework---outperforming evaluation kits, which are limited to documented and vendor-defined functions. The modified Bluetooth stack remains fully functional and high-performance. Hence, it provides a portable low-cost research platform. InternalBlue is a versatile framework and we demonstrate its abilities by implementing tests and demos for known Bluetooth vulnerabilities. Moreover, we discover a novel critical security issue affecting a large selection of Broadcom chipsets that allows executing code within the attacked Bluetooth firmware. We further show how to use our framework to fix bugs in chipsets out of vendor support and how to add new security features to Bluetooth firmware

An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

Wireless body sensor networks for health-monitoring applications

This is an author-created, un-copyedited version of an article accepted for publication in Physiological Measurement. The publisher is not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at http://dx.doi.org/10.1088/0967-3334/29/11/R01

LEGaTO: first steps towards energy-efficient toolset for heterogeneous computing

LEGaTO is a three-year EU H2020 project which started in December 2017. The LEGaTO project will leverage task-based programming models to provide a software ecosystem for Made-in-Europe heterogeneous hardware composed of CPUs, GPUs, FPGAs and dataflow engines. The aim is to attain one order of magnitude energy savings from the edge to the converged cloud/HPC.Peer ReviewedPostprint (author's final draft