Low-complexity Multiclass Encryption by Compressed Sensing

The idea that compressed sensing may be used to encrypt information from unauthorised receivers has already been envisioned, but never explored in depth since its security may seem compromised by the linearity of its encoding process. In this paper we apply this simple encoding to define a general private-key encryption scheme in which a transmitter distributes the same encoded measurements to receivers of different classes, which are provided partially corrupted encoding matrices and are thus allowed to decode the acquired signal at provably different levels of recovery quality. The security properties of this scheme are thoroughly analysed: firstly, the properties of our multiclass encryption are theoretically investigated by deriving performance bounds on the recovery quality attained by lower-class receivers with respect to high-class ones. Then we perform a statistical analysis of the measurements to show that, although not perfectly secure, compressed sensing grants some level of security that comes at almost-zero cost and thus may benefit resource-limited applications. In addition to this we report some exemplary applications of multiclass encryption by compressed sensing of speech signals, electrocardiographic tracks and images, in which quality degradation is quantified as the impossibility of some feature extraction algorithms to obtain sensitive information from suitably degraded signal recoveries.Comment: IEEE Transactions on Signal Processing, accepted for publication. Article in pres

On Known-Plaintext Attacks to a Compressed Sensing-based Encryption: A Quantitative Analysis

Despite the linearity of its encoding, compressed sensing may be used to provide a limited form of data protection when random encoding matrices are used to produce sets of low-dimensional measurements (ciphertexts). In this paper we quantify by theoretical means the resistance of the least complex form of this kind of encoding against known-plaintext attacks. For both standard compressed sensing with antipodal random matrices and recent multiclass encryption schemes based on it, we show how the number of candidate encoding matrices that match a typical plaintext-ciphertext pair is so large that the search for the true encoding matrix inconclusive. Such results on the practical ineffectiveness of known-plaintext attacks underlie the fact that even closely-related signal recovery under encoding matrix uncertainty is doomed to fail. Practical attacks are then exemplified by applying compressed sensing with antipodal random matrices as a multiclass encryption scheme to signals such as images and electrocardiographic tracks, showing that the extracted information on the true encoding matrix from a plaintext-ciphertext pair leads to no significant signal recovery quality increase. This theoretical and empirical evidence clarifies that, although not perfectly secure, both standard compressed sensing and multiclass encryption schemes feature a noteworthy level of security against known-plaintext attacks, therefore increasing its appeal as a negligible-cost encryption method for resource-limited sensing applications.Comment: IEEE Transactions on Information Forensics and Security, accepted for publication. Article in pres

Rakeness in the design of Analog-to-Information Conversion of Sparse and Localized Signals

Design of Random Modulation Pre-Integration systems based on the restricted-isometry property may be suboptimal when the energy of the signals to be acquired is not evenly distributed, i.e. when they are both sparse and localized. To counter this, we introduce an additional design criterion, that we call rakeness, accounting for the amount of energy that the measurements capture from the signal to be acquired. Hence, for localized signals a proper system tuning increases the rakeness as well as the average SNR of the samples used in its reconstruction. Yet, maximizing average SNR may go against the need of capturing all the components that are potentially non-zero in a sparse signal, i.e., against the restricted isometry requirement ensuring reconstructability. What we propose is to administer the trade-off between rakeness and restricted isometry in a statistical way by laying down an optimization problem. The solution of such an optimization problem is the statistic of the process generating the random waveforms onto which the signal is projected to obtain the measurements. The formal definition of such a problems is given as well as its solution for signals that are either localized in frequency or in more generic domain. Sample applications, to ECG signals and small images of printed letters and numbers, show that rakeness-based design leads to non-negligible improvements in both cases

Computational Flux Balance Analysis Predicts that Stimulation of Energy Metabolism in Astrocytes and their Metabolic Interactions with Neurons Depend on Uptake of K(+) Rather than Glutamate

Brain activity involves essential functional and metabolic interactions between neurons and astrocytes. The importance of astrocytic functions to neuronal signaling is supported by many experiments reporting high rates of energy consumption and oxidative metabolism in these glial cells. In the brain, almost all energy is consumed by the Na(+)/K(+) ATPase, which hydrolyzes 1 ATP to move 3 Na(+) outside and 2 K(+) inside the cells. Astrocytes are commonly thought to be primarily involved in transmitter glutamate cycling, a mechanism that however only accounts for few % of brain energy utilization. In order to examine the participation of astrocytic energy metabolism in brain ion homeostasis, here we attempted to devise a simple stoichiometric relation linking glutamatergic neurotransmission to Na(+) and K(+) ionic currents. To this end, we took into account ion pumps and voltage/ligand-gated channels using the stoichiometry derived from available energy budget for neocortical signaling and incorporated this stoichiometric relation into a computational metabolic model of neuron-astrocyte interactions. We aimed at reproducing the experimental observations about rates of metabolic pathways obtained by (13)C-NMR spectroscopy in rodent brain. When simulated data matched experiments as well as biophysical calculations, the stoichiometry for voltage/ligand-gated Na(+) and K(+) fluxes generated by neuronal activity was close to a 1:1 relationship, and specifically 63/58 Na(+)/K(+) ions per glutamate released. We found that astrocytes are stimulated by the extracellular K(+) exiting neurons in excess of the 3/2 Na(+)/K(+) ratio underlying Na(+)/K(+) ATPase-catalyzed reaction. Analysis of correlations between neuronal and astrocytic processes indicated that astrocytic K(+) uptake, but not astrocytic Na(+)-coupled glutamate uptake, is instrumental for the establishment of neuron-astrocytic metabolic partnership. Our results emphasize the importance of K(+) in stimulating the activation of astrocytes, which is relevant to the understanding of brain activity and energy metabolism at the cellular level. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11064-016-2048-0) contains supplementary material, which is available to authorized users

Through-The-Barrier Communications in Isolated Class-E Converters Embedding a Low-k Transformer

In a recent paper, a through-the-barrier communication technique suitable for isolated resonant converters has been proposed. The approach is capable of sending data bidirectionally at high speed (one bit for each converter clock period) without the need of any additional isolating device other than the transformer necessary for the power transfer, and has been demonstrated by means of a proof-of-concept low-frequency prototype. In this paper we review that work under the assumption of increasing the operating frequency by using a coreless transformer presenting low losses, but also a low coupling factor k. This allows to increase the efficiency of the converter to a very high value (92% in the proposed design working at 6.78 MHz), but the communication speed has to be reduced (one bit every four clock cycles)

Rakeness-based Compressed Sensing of Surface ElectroMyoGraphy for Improved Hand Movement Recognition in the Compressed Domain

Surface electromyography (sEMG) waveforms are widely used to generate control signals in several application areas, ranging from prosthetic to consumer electronics. Classically, such waveforms are acquired at Nyquist rate and digitally transmitted trough a wireless channel to a decision/actuation node. This causes large energy consumption and is incompatible with the implementation of ultra-low power acquisition nodes. We already proposed Compressed Sensing (CS) as a low-complexity method to achieve substantial energy saving by reducing the size of data to be transmitted while preserving the information content. We here make a significant leap forward by showing that hand movements recognition task can be performed directly in the compressed domain with a success rate greater than 98 % and with a reduction of the number of transmitted bits by two order of magnitude with respect to row data

A Unified Design Theory for Class-E Resonant DC–DC Converter Topologies

Resonant and quasi-resonant dc-dc converters have been introduced to increase the operating frequency of switching power converters, with advantages in terms of performance, cost, and/or size. In this paper, we focus on class-E resonant topologies, and we show that about twenty different architectures proposed in the last three decades can be reduced to two basic topologies, allowing the extension to all these resonant converters of an exact and straightforward design procedure that has been recently proposed. This represents an important breakthrough with respect to the state of the art, where class-E circuit analysis is always based on strong simplifying assumptions, and the final circuit design is achieved by means of numerical simulations. The potentialities of the proposed exact methodology are highlighted by realistic circuit-level simulations, where the desired waveforms are obtained in one single step without the need of a time-consuming iterative trial-and-error process

A Non-conventional Sum-and-Max based Neural Network layer for Low Power Classification

The increasing need for small and low-power Deep Neural Networks (DNNs) for edge computing applications involves the investigation of new architectures that allow good performance on low-resources/mobile devices. To this aim, many different structures have been proposed in the literature, mainly targeting the reduction in the costs introduced by the Multiply and Accumulate (MAC) primitive. In this work, a DNN layer based on the novel Sum and Max (SAM) paradigm is proposed. It does not require either the use of multiplications or the insertion of complex non-linear operations. Furthermore, it is especially prone to aggressive pruning, thus needing a very low number of parameters to work. The layer is tested on a simple classification task and its cost is compared with a classic DNN layer with equivalent accuracy based on the MAC primitive, in order to assess the reduction of resources that the use of this new structure could introduce

Aggressively prunable MAM²-based Deep Neural Oracle for ECG acquisition by Compressed Sensing

The growing interest in Internet of Things (IoT) and mobile biomedical applications is pushing the investigation on approaches that can be used to reduce the energy consumption while acquiring data. Compressed Sensing (CS) is a technique that allows to reduce the energy required for the acquisition and compression of a sparse signal, transferring the complexity to the reconstruction stage. Many works leverage the use of Deep Neural Networks (DNNs) for signal reconstruction and, assuming that also this operation has to be performed on a IoT device, it is necessary for the DNN architecture to fit in small and low-energy devices. Pruning techniques, that can reduce the size of DNNs by removing unnecessary parameters and thus decreasing storage requirements, can be of great help in this effort. In this work, a novel Multiply and Max&Min (MAM²) map-reduce paradigm trained with the vanishing contributes technique and then pruned with the activation rate method is proposed. The result is a naturally and aggressively pruned DNN layer structure. This structure is used to reduce the complexity of a DNN-based CS reconstructor and its performance is verified. As an example, MAM²-based layers still retain the baseline accuracy of the CS decoder with 94% of the parameters pruned against 25% when using classic MAC-based layers only

Rakeness-Based Compressed Sensing of Multiple-graph Signals for IoT Applications

Signals on multiple graphs may model IoT scenarios consisting of a local wireless sensor network performing sets of acquisitions that must be sent to a central hub that may be far from the measurement field. Rakeness-based design of compressed sensing is exploited to allow the administration of the tradeoff between local communication and the long-range transmission needed to reach the hub. Extensive Monte Carlo simulations incorporating real world figures in terms of communication consumption show a potential energy saving from 25% to almost 50% with respect to a direct approach not exploiting local communication and rakeness