A system architecture, processor, and communication protocol for secure implants

Secure and energy-efficient communication between Implantable Medical Devices (IMDs) and authorized external users is attracting increasing attention these days. However, there currently exists no systematic approach to the problem, while solutions from neighboring fields, such as wireless sensor networks, are not directly transferable due to the peculiarities of the IMD domain. This work describes an original, efficient solution for secure IMD communication. A new implant system architecture is proposed, where security and main-implant functionality are made completely decoupled by running the tasks onto two separate cores. Wireless communication goes through a custom security ASIP, called SISC (Smart-Implant Security Core), which runs an energy-efficient security protocol. The security core is powered by RF-harvested energy until it performs external-reader authentication, providing an elegant defense mechanism agai

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective: The advent of High-Performance Computing (HPC) in recent years has led to its increasing use in brain study through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a single acceleration (or homogeneous) platform to effectively address the complete array of modeling requirements. Approach: In this paper we propose and build BrainFrame, a heterogeneous acceleration platform, incorporating three distinct acceleration technologies, a Dataflow Engine, a Xeon Phi and a GP-GPU. The PyNN framework is also integrated into the platform. As a challenging proof of concept, we analyze the performance of BrainFrame on different instances of a state-of-the-art neuron model, modeling the Inferior- Olivary Nucleus using a biophysically-meaningful, extended Hodgkin-Huxley representation. The model instances take into account not only the neuronal- network dimensions but also different network-connectivity circumstances that can drastically change application workload characteristics. Main results: The synthetic approach of three HPC technologies demonstrated that BrainFrame is better able to cope with the modeling diversity encountered. Our performance analysis shows clearly that the model directly affect performance and all three technologies are required to cope with all the model use cases.Comment: 16 pages, 18 figures, 5 table

Peak misdetection in heart-beat-based security: Characterization and tolerance

Abstract — The Inter-Pulse-Interval (IPI) of heart beats has previously been suggested for security in mobile health (mHealth) applications. In IPI-based security, secure communi-cation is facilitated through a security key derived from the time difference between heart beats. However, there currently exists no work which considers the effect on security of imperfect heart-beat (peak) detection. This is a crucial aspect of IPI-based security and likely to happen in a real system. In this paper, we evaluate the effects of peak misdetection on the security performance of IPI-based security. It is shown that even with a high peak detection rate between 99.9 % and 99.0%, a significant drop in security performance may be observed (between-70 % and-303%) compared to having perfect peak detection. We show that authenticating using smaller keys yields both stronger keys as well as potentially faster authentication in case of imperfect heart beat detection. Finally, we present an algorithm which tolerates the effect of a single misdetected peak and increases the security performance by up to 155%. I

BrainFrame: A node-level heterogeneous accelerator platform for neuron simulations

Objective. The advent of high-performance computing (HPC) in recent years has led to its increasing use in brain studies through computational models. The scale and complexity of such models are constantly increasing, leading to challenging computational requirements. Even though modern HPC platforms can often deal with such challenges, the vast diversity of the modeling field does not permit for a homogeneous acceleration platform to effectively address the complete array of modeling requirements. Approach. In this paper we propose and build BrainFrame, a heterogeneous acceleration platform that incorporates three distinct acceleration technologies, an Intel Xeon-Phi CPU

Dirichlet sigma models and mean curvature flow

The mean curvature flow describes the parabolic deformation of embedded branes in Riemannian geometry driven by their extrinsic mean curvature vector, which is typically associated to surface tension forces. It is the gradient flow of the area functional, and, as such, it is naturally identified with the boundary renormalization group equation of Dirichlet sigma models away from conformality, to lowest order in perturbation theory. D-branes appear as fixed points of this flow having conformally invariant boundary conditions. Simple running solutions include the paper-clip and the hair-pin (or grim-reaper) models on the plane, as well as scaling solutions associated to rational (p, q) closed curves and the decay of two intersecting lines. Stability analysis is performed in several cases while searching for transitions among different brane configurations. The combination of Ricci with the mean curvature flow is examined in detail together with several explicit examples of deforming curves on curved backgrounds. Some general aspects of the mean curvature flow in higher dimensional ambient spaces are also discussed and obtain consistent truncations to lower dimensional systems. Selected physical applications are mentioned in the text, including tachyon condensation in open string theory and the resistive diffusion of force-free fields in magneto-hydrodynamics.Comment: 77 pages, 21 figure

Enhancing heart-beat-based security for mHealth applications

In heart-beat-based security, a security key is derived from the time difference between two consecutive heart beats (the Inter-Pulse-Interval, IPI) which may, subsequently, be used to enable secure communication. While heart-beatbased security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects which should be considered in practice, including subjects with reduced heart-rate variability, different sensor-sampling frequencies, inter-sensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-caseauthentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, lessentropic keys may actually increase the key strength by reducing the effects of inter-sensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject\u27s heart-rate variability. To improve security, we introduce the Inter-multi-Pulse Interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between two non-consecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4x (+19.2 bits) for subjects with limited heart-rate variability, at the cost of an extended key-generation time of 4.8x (+45 sec)

Adaptive entity-identifier generation for IMD emergency access

Recent work on wireless Implantable Medical Devices (IMDs) has revealed the need for secure communication in order to prevent data theft and implant abuse by malicious attackers. However, security should not be provided at the cost of patient safety and an IMD should, thus, remain accessible during an emergency regardless of device security. In this paper, we present a novel method of providing IMD emergency access, based on generating Entity Identifiers (EI) using the Inter-Pulse Intervals (IPIs) of heartbeats. We evaluate the current state-of-the-art in EI-generation in terms of security and accessibility for healthy subjects with a wide range of heart rates. Subsequently, we present an adaptive EI-generation algorithm which takes the heart rate into account, maintaining an acceptable emergency-mode activation time (between 5-55.4 s) while improving security by up to 3.4x for high heart rates. Finally, we show that activating emergency mode may consume as little as 0.24μJ from the IMD battery

Enhancing Heart-Beat-Based Security for mHealth Applications

In heart-beat-based security, a security key is derived from the time difference between consecutive heart beats (the inter-pulse interval, IPI), which may, subsequently, be used to enable secure communication. While heart-beat-based security holds promise in mobile health (mHealth) applications, there currently exists no work that provides a detailed characterization of the delivered security in a real system. In this paper, we evaluate the strength of IPI-based security keys in the context of entity authentication. We investigate several aspects that should be considered in practice, including subjects with reduced heart-rate variability (HRV), different sensor-sampling frequencies, intersensor variability (i.e., how accurate each entity may measure heart beats) as well as average and worst-case-authentication time. Contrary to the current state of the art, our evaluation demonstrates that authentication using multiple, less-entropic keys may actually increase the key strength by reducing the effects of intersensor variability. Moreover, we find that the maximal key strength of a 60-bit key varies between 29.2 bits and only 5.7 bits, depending on the subject's HRV. To improve security, we introduce the inter-multi-pulse interval (ImPI), a novel method of extracting entropy from the heart by considering the time difference between nonconsecutive heart beats. Given the same authentication time, using the ImPI for key generation increases key strength by up to 3.4 × (+19.2 bits) for subjects with limited HRV, at the cost of an extended key-generation time of 4.8 × (+45 s)

Performance Analysis of Accelerated Biophysically-Meaningful Neuron Simulations

In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads