825 research outputs found
Reflections on the future of research curation and research reproducibility
In the years since the launch of the World Wide Web in 1993, there have been profoundly transformative changes to the entire concept of publishing—exceeding all the previous combined technical advances of the centuries following the introduction of movable type in medieval Asia around the year 10001 and the subsequent large-scale commercialization of printing several centuries later by J. Gutenberg (circa 1440). Periodicals in print—from daily newspapers to scholarly journals—are now quickly disappearing, never to return, and while no publishing sector has been unaffected, many scholarly journals are almost unrecognizable in comparison with their counterparts of two decades ago. To say that digital delivery of the written word is fundamentally different is a huge understatement. Online publishing permits inclusion of multimedia and interactive content that add new dimensions to what had been available in print-only renderings. As of this writing, the IEEE portfolio of journal titles comprises 59 online only2 (31%) and 132 that are published in both print and online. The migration from print to online is more stark than these numbers indicate because of the 132 periodicals that are both print and online, the print runs are now quite small and continue to decline. In short, most readers prefer to have their subscriptions fulfilled by digital renderings only
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
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
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
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
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