arXiv subjects

Marco Baldi

Publications and source records attributed to Marco Baldi.

At least 91 records · Page 5Linked to original sources

arXiv2019

Security analysis of a blockchain-based protocol for the certification of academic credentials

We consider a blockchain-based protocol for the certification of academic credentials named Blockcerts, which is currently used worldwide for validating digital certificates of competence compliant with the Open Badges standard. We study the certification steps that are performed by the Blockcerts protocol to validate a certificate, and find that they are vulnerable to a certain type of impersonation attacks. More in detail, authentication of the issuing institution is performed by retrieving an unauthenticated issuer profile online, and comparing some data reported there with those included in the issued certificate. We show that, by fabricating a fake issuer profile and generating a suitably altered certificate, an attacker is able to impersonate a legitimate issuer and can produce certificates that cannot be distinguished from originals by the Blockcerts validation procedure. We also propose some possible countermeasures against an attack of this type, which require the use of a classic public key infrastructure or a decentralized identity system integrated with the Blockcerts protocol.

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arXiv2019

Statistical and Machine Learning-based Decision Techniques for Physical Layer Authentication

In this paper we assess the security performance of key-less physical layer authentication schemes in the case of time-varying fading channels, considering both partial and no channel state information (CSI) on the receiver's side. We first present a generalization of a well-known protocol previously proposed for flat fading channels and we study different statistical decision methods and the corresponding optimal attack strategies in order to improve the authentication performance in the considered scenario. We then consider the application of machine learning techniques in the same setting, exploiting different one-class nearest neighbor (OCNN) classification algorithms. We observe that, under the same probability of false alarm, one-class classification (OCC) algorithms achieve the lowest probability of missed detection when a low spatial correlation exists between the main channel and the adversary one, while statistical methods are advantageous when the spatial correlation between the two channels is higher.

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arXiv2019

Fast numerical method to generate halo catalogs in modified gravity (part I): second-order Lagrangian Perturbation Theory

We present and test a new numerical method to determine second-order Lagrangian displacement fields in the context of modified gravity (MG) theories. We start from the extension of Lagrangian Perturbation Theory to a class of MG models that can be described by a parametrized Poisson equation, with the introduction of a scale-dependent function. We exploit fast Fourier transforms to compute the full source term of the differential equation for the second-order Lagrangian displacement field. We compare its mean to the source term computed for specific configurations for which a k-dependent solution can be found numerically. We choose the configuration that best matches the full source term, thus obtaining an approximate factorization of the second-order displacement field as the space term valid for standard gravity times a k-dependent, second-order growth factor $D_2(k,t)$. This approximation is used to compute second order displacements for particles. The method is tested against N-body simulations run with standard and $f(R)$ gravity: we rely on the results of a friends-of-friends code run on the N-body snapshots to assign particles to halos, then compute the halo power spectrum. We find very consistent results for the two gravity theories: second-order LPT (2LPT) allows to recover the halo power spectrum of N-body simulations within $\sim 10\%$ precision to $k\sim 0.2-0.4\ h\ {\rm Mpc}^{-1}$, as well as halo positions, with an error that is a fraction of the inter-particle distance. We show that, when considering the same level of non-linearity in the density field, the performance of 2LPT with MG is the same (within $1\%$) as the one obtained for the standard $Λ$CDM model with General Relativity. When implemented in a computer code, this formulation of 2LPT can quickly generate dark matter distributions with $f(R)$ gravity, and can easily be extended to other MG theories.

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arXiv2019

Cosmological exploitation of the size function of cosmic voids identified in the distribution of biased tracers

Cosmic voids are large underdense regions that, together with galaxy clusters, filaments and walls, build up the large-scale structure of the Universe. The void size function provides a powerful probe to test the cosmological framework. However, to fully exploit this statistics, the void sample has to be properly cleaned from spurious objects. Furthermore, the bias of the mass tracers used to detect these regions has to be taken into account in the size function model. In our work we test a cleaning algorithm and a new void size function model on a set of simulated dark matter halo catalogues, with different mass and redshift selections, to investigate the statistics of voids identified in a biased mass density field. We then investigate how the density field tracers' bias affects the detected size of voids. The main result of this analysis is a new model of the size function, parameterised in terms of the linear effective bias of the tracers used, which is straightforwardly inferred from the large-scale two-point correlation function. This represents a crucial step to exploit the method on real data catalogues. The proposed size function model has been accurately calibrated on mock catalogues, and used to validate the possibility to provide forecasts on the cosmological constraints, namely on the matter density contrast, $Ω_{\rm M}$, and on the normalisation of the linear matter power spectrum, $σ_8$, at different redshifts.

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arXiv2019

Clustering and redshift-space distortions in modified gravity models with massive neutrinos

Modified gravity and massive neutrino cosmologies are two of the most interesting scenarios that have been recently explored to account for possible observational deviations from the concordance $Λ$-cold dark matter ($Λ$CDM) model. In this context, we investigated the large-scale structure of the Universe by exploiting the \dustp simulations that implement, simultaneously, the effects of $f(R)$ gravity and massive neutrinos. To study the possibility of breaking the degeneracy between these two effects, we analysed the redshift-space distortions in the clustering of dark matter haloes at different redshifts. Specifically, we focused on the monopole and quadrupole of the two-point correlation function, both in real and redshift space. The deviations with respect to $Λ$CDM model have been quantified in terms of the linear growth rate parameter. We found that redshift-space distortions provide a powerful probe to discriminate between $Λ$CDM and modified gravity models, especially at high redshifts ($z \gtrsim 1$), even in the presence of massive neutrinos.

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arXiv2019

Distinguishing standard and modified gravity cosmologies with machine learning

We present a convolutional neural network to classify distinct cosmological scenarios based on the statistically similar weak-lensing maps they generate. Modified gravity (MG) models that include massive neutrinos can mimic the standard concordance model ($Λ$CDM) in terms of Gaussian weak-lensing observables. An inability to distinguish viable models that are based on different physics potentially limits a deeper understanding of the fundamental nature of cosmic acceleration. For a fixed redshift of sources, we demonstrate that a machine learning network trained on simulated convergence maps can discriminate between such models better than conventional higher-order statistics. Results improve further when multiple source redshifts are combined. To accelerate training, we implement a novel data compression strategy that incorporates our prior knowledge of the morphology of typical convergence map features. Our method fully distinguishes $Λ$CDM from its most similar MG model on noise-free data, and it correctly identifies among the MG models with at least 80% accuracy when using the full redshift information. Adding noise lowers the correct classification rate of all models, but the neural network still significantly outperforms the peak statistics used in a previous analysis.

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arXiv2019

Analysis of reaction and timing attacks against cryptosystems based on sparse parity-check codes

In this paper we study reaction and timing attacks against cryptosystems based on sparse parity-check codes, which encompass low-density parity-check (LDPC) codes and moderate-density parity-check (MDPC) codes. We show that the feasibility of these attacks is not strictly associated to the quasi-cyclic (QC) structure of the code but is related to the intrinsically probabilistic decoding of any sparse parity-check code. So, these attacks not only work against QC codes, but can be generalized to broader classes of codes. We provide a novel algorithm that, in the case of a QC code, allows recovering a larger amount of information than that retrievable through existing attacks and we use this algorithm to characterize new side-channel information leakages. We devise a theoretical model for the decoder that describes and justifies our results. Numerical simulations are provided that confirm the effectiveness of our approach.

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arXiv2019

Efficient Search and Elimination of Harmful Objects in Optimized QC SC-LDPC Codes

The error correction performance of low-density parity-check (LDPC) codes under iterative message-passing decoding is degraded by the presence of certain harmful objects existing in their Tanner graph representation. Depending on the context, such harmful objects are known as stopping sets, trapping sets, absorbing sets, or pseudocodewords. In this paper, we propose a general procedure based on edge spreading that enables the design of quasi-cyclic (QC) spatially coupled low-density parity-check codes (SC-LDPCCs) that are derived from QC-LDPC block codes and possess a significantly reduced multiplicity of harmful objects with respect to the original QC-LDPC block code. This procedure relies on a novel algorithm that greedily spans the search space of potential candidates to reduce the multiplicity of the target harmful object(s) in the Tanner graph. The effectiveness of the method we propose is validated via examples and numerical computer simulations.

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arXiv2019

On the dissection of degenerate cosmologies with machine learning

Based on the DUSTGRAIN-pathfinder suite of simulations, we investigate observational degeneracies between nine models of modified gravity and massive neutrinos. Three types of machine learning techniques are tested for their ability to discriminate lensing convergence maps by extracting dimensional reduced representations of the data. Classical map descriptors such as the power spectrum, peak counts and Minkowski functionals are combined into a joint feature vector and compared to the descriptors and statistics that are common to the field of digital image processing. To learn new features directly from the data we use a Convolutional Neural Network (CNN). For the mapping between feature vectors and the predictions of their underlying model, we implement two different classifiers; one based on a nearest-neighbour search and one that is based on a fully connected neural network. We find that the neural network provides a much more robust classification than the nearest-neighbour approach and that the CNN provides the most discriminating representation of the data. It achieves the cleanest separation between the different models and the highest classification success rate of 59% for a single source redshift. Once we perform a tomographic CNN analysis, the total classification accuracy increases significantly to 76% with no observational degeneracies remaining. Visualising the filter responses of the CNN at different network depths provides us with the unique opportunity to learn from very complex models and to understand better why they perform so well.

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arXiv2019

Cosmic voids uncovered -- first-order statistics of depressions in the biased density field

Cosmic voids are the major volume component in the matter distribution of the Universe. They posses great potential for constraining dark energy as well as for testing theories of gravity. Nevertheless, in spite of their growing popularity as cosmological probes, a gap of knowledge between cosmic void observations and theory still persists. In particular, the void size function models proposed in literature have been proven unsuccessful in reproducing the results obtained from cosmological simulations in which cosmic voids are detected from biased tracers of the density field, undermining the possibility of using them as cosmological probes. The goal of this work is to cover this gap. In particular, we make use of the findings of a previous work in which we have improved the void selection procedure, presenting an algorithm that redefines the void ridges and, consequently, their radius. By applying this algorithm, we validate the volume conserving model of the void size function on a set of unbiased simulated density field tracers. We highlight the difference in the internal structure between voids selected in this way and those identified by the popular VIDE void finder. We also extend the validation of the model to the case of biased tracers. We find that a relation exists between the tracer used to sample the underlying dark matter density field and its unbiased counterpart. Moreover, we demonstrate that, as long as this relation is accounted for, the size function is a viable approach for studying cosmology with cosmic voids.

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arXiv2019

Breaking cosmic degeneracies: Disentangling neutrinos and modified gravity with kinematic information

Searches for modified gravity in the large-scale structure try to detect the enhanced amplitude of density fluctuations caused by the fifth force present in many of these theories. Neutrinos, on the other hand, suppress structure growth below their free-streaming length. Both effects take place on comparable scales, and uncertainty in the neutrino mass leads to a degeneracy with modified gravity parameters for probes that are measuring the amplitude of the matter power spectrum. We explore the possibility to break the degeneracy between modified gravity and neutrino effects in the growth of structures by considering kinematic information related to either the growth rate on large scales or the virial velocities inside of collapsed structures. In order to study the degeneracy up to fully non-linear scales, we employ a suite of $N$-body simulations including both $f(R)$ modified gravity and massive neutrinos. Our results indicate that velocity information provides an excellent tool to distinguish massive neutrinos from modified gravity. Models with different values of neutrino masses and modified gravity parameters possessing a comparable matter power spectrum at a given time have different growth rates. This leaves imprints in the velocity divergence, which is therefore better suited than the amplitude of density fluctuations to tell the models apart. In such models with a power spectrum comparable to $Λ$CDM today, the growth rate is strictly enhanced. We also find the velocity dispersion of virialised clusters to be well suited to constrain deviations from general relativity without being affected by the uncertainty in the sum of neutrino masses.

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arXiv2019

Cryptanalysis of a One-Time Code-Based Digital Signature Scheme

We consider a one-time digital signature scheme recently proposed by Persichetti and show that a successful key recovery attack can be mounted with limited complexity. The attack we propose exploits a single signature intercepted by the attacker, and relies on a statistical analysis performed over such a signature, followed by information set decoding. We assess the attack complexity and show that a full recovery of the secret key can be performed with a work factor that is far below the claimed security level. The efficiency of the attack is motivated by the sparsity of the signature, which leads to a significant information leakage about the secret key.

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arXiv2018

Breaking degeneracies in modified gravity with higher (than 2nd) order weak-lensing statistics

General relativity (GR) has been well tested up to solar system scales, but it is much less certain that standard gravity remains an accurate description on the largest, that is, cosmological, scales. Many extensions to GR have been studied that are not yet ruled out by the data, including by that of the recent direct gravitational wave detections. Degeneracies among the standard model ($Λ$CDM) and modified gravity (MG) models, as well as among different MG parameters, must be addressed in order to best exploit information from current and future surveys and to unveil the nature of dark energy. We propose various higher-order statistics in the weak-lensing signal as a new set of observables able to break degeneracies between massive neutrinos and MG parameters. We have tested our methodology on so-called $f(R)$ models, which constitute a class of viable models that can explain the accelerated universal expansion by a modification of the fundamental gravitational interaction. We have explored a range of these models that still fit current observations at the background and linear level, and we show using numerical simulations that certain models which include massive neutrinos are able to mimic $Λ$CDM in terms of the 3D power spectrum of matter density fluctuations. We find that depending on the redshift and angular scale of observation, non-Gaussian information accessed by higher-order weak-lensing statistics can be used to break the degeneracy between $f(R)$ models and $Λ$CDM. In particular, peak counts computed in aperture mass maps outperform third- and fourth-order moments.

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arXiv2018

Lyman-$α$ forest and non-linear structure characterization in Fuzzy Dark Matter cosmologies

Fuzzy Dark Matter (FDM) represents an alternative and intriguing description of the standard Cold Dark Matter (CDM) fluid, able to explain the lack of direct detection of dark matter particles in the GeV sector and to alleviate small scales tensions in the cosmic large-scale structure formation. Cosmological simulations of FDM models in the literature were performed either with very expensive high-resolution grid-based simulations of individual haloes or through N-body simulations encompassing larger cosmic volumes but resorting on significant approximations in the FDM non-linear dynamics to reduce their computational cost. With the use of the new N-body cosmological hydrodynamical code AX-GADGET, we are now able not only to overcome such numerical problems, but also to combine a fully consistent treatment of FDM dynamics with the presence of gas particles and baryonic physical processes, in order to quantify the FDM impact on specific astrophysical observables. In particular, in this paper we perform and analyse several hydrodynamical simulations in order to constrain the FDM mass by quantifying the impact of FDM on Lyman-$α$ forest observations, as obtained for the first time in the literature in a N-body setup without approximating the FDM dynamics. We also study the statistical properties of haloes, exploiting the large available sample, to extract information on how FDM affects the abundance, the shape, and density profiles of dark matter haloes.

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arXiv2018

Compact QC-LDPC Block and SC-LDPC Convolutional Codes for Low-Latency Communications

Low decoding latency and complexity are two important requirements of channel codes used in many applications, like machine-to-machine communications. In this paper, we show how these requirements can be fulfilled by using some special quasi-cyclic low-density parity-check block codes and spatially coupled low-density parity-check convolutional codes that we denote as compact. They are defined by parity-check matrices designed according to a recent approach based on sequentially multiplied columns. This method allows obtaining codes with girth up to 12. Many numerical examples of practical codes are provided.

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arXiv2018

Weak Lensing Light-Cones in Modified Gravity simulations with and without Massive Neutrinos

We present a novel suite of cosmological N-body simulations called the DUSTGRAIN-pathfinder, implementing simultaneously the effects of an extension to General Relativity in the form of $f(R)$ gravity and of a non-negligible fraction of massive neutrinos. We describe the generation of simulated weak lensing and cluster counts observables within a past light-cone extracted from these simulations. The simulations have been performed by means of a combination of the MG-GADGET code and a particle-based implementation of massive neutrinos, while the light-cones have been generated using the MapSim pipeline allowing us to compute weak lensing maps through a ray-tracing algorithm for different values of the source plane redshift. The mock observables extracted from our simulations will be employed for a series of papers focussed on understanding and possibly breaking the well-known observational degeneracy between $f(R)$ gravity and massive neutrinos, i.e. the fact that some specific combinations of the characteristic parameters for these two phenomena (the $f_{R0}$ scalar amplitude and the total neutrino mass $Σm_ν$) may result indistinguishable from the standard $\mathrm{ΛCDM}$ cosmology through several standard observational probes. In particular, in the present work we show how a tomographic approach to weak lensing statistics could allow - especially for the next generation of wide-field surveys - to disentangle some of the models that appear statistically indistinguishable through standard single-redshift weak lensing probe.

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arXiv2018

Assessing and countering reaction attacks against post-quantum public-key cryptosystems based on QC-LDPC codes

Code-based public-key cryptosystems based on QC-LDPC and QC-MDPC codes are promising post-quantum candidates to replace quantum vulnerable classical alternatives. However, a new type of attacks based on Bob's reactions have recently been introduced and appear to significantly reduce the length of the life of any keypair used in these systems. In this paper we estimate the complexity of all known reaction attacks against QC-LDPC and QC-MDPC code-based variants of the McEliece cryptosystem. We also show how the structure of the secret key and, in particular, the secret code rate affect the complexity of these attacks. It follows from our results that QC-LDPC code-based systems can indeed withstand reaction attacks, on condition that some specific decoding algorithms are used and the secret code has a sufficiently high rate.

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arXiv2018

Resource Allocation for Secure Gaussian Parallel Relay Channels with Finite-Length Coding and Discrete Constellations

We investigate the transmission of a secret message from Alice to Bob in the presence of an eavesdropper (Eve) and many of decode-and-forward relay nodes. Each link comprises a set of parallel channels, modeling for example an orthogonal frequency division multiplexing transmission. We consider the impact of discrete constellations and finite-length coding, defining an achievable secrecy rate under a constraint on the equivocation rate at Eve. Then we propose a power and channel allocation algorithm that maximizes the achievable secrecy rate by resorting to two coupled Gale-Shapley algorithms for stable matching problem. We consider the scenarios of both full and partial channel state information at Alice. In the latter case, we only guarantee an outage secrecy rate, i.e., the rate of a message that remains secret with a given probability. Numerical results are provided for Rayleigh fading channels in terms of average outage secrecy rate, showing that practical schemes achieve a performance quite close to that of ideal ones.

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