KP+ : Fixing Availability Issues on KP Ownership Transfer Protocols

Ownership Transfer Protocols for RFID allow transferring the rights over a tag from a current owner to a new owner in a secure and private way. Recently, Kapoor and Piramuthu have proposed two schemes which solve most of the security weaknesses detected in previously published protocols. However, this paper reviews this work and points out that such schemes still present some practical and security issues. We then propose some modifications in these protocols that overcome such problems

Distributed Group Authentication for RFID Supply Management

We investigate an application of Radio Frequency Identification (RFID) referred to in the literature as group scanning, in which an RFID reader device interrogates several RFID tags to establish “simultaneous” presence of a group of tags. Our goal is to study the group scanning problem in strong adversarial settings and show how group scanning can be used in distributed applications for supply chain management. We present a security framework for group scanning and give a formal description of the attending security requirements. Our model is based on the Universal Composability framework and supports re-usability (through modularity of security guarantees). We propose two novel protocols that realize group scanning in this security model, based on off-the-shelf components such as low-cost (highly optimized) pseudorandom functions, and show how these can be integrated into RFID supply-chain management system

Secure Distributed System inspired by Ant Colonies for Road Traffic Management in Emergency Situations

We have proposed an algorithm, based on ant colonies, for road traffic management. The implementation of the algorithm does not rely on fixed infrastructures in order to operate in emergency situations. It only uses the VANET V2V communications and location systems that do not require contact with a fixed infrastructure. The algorithm uses signature aggregation and reputation lists to ensure system security. Furthermore, the algorithm has an implicit security that minimizes the risks in case of attacks. A scale prototype has been designed and implemented to validate the algorithm using RFID location system.In this work, we present a distributed system designed for road traffic management. The system is inspired by the behavior of the ant colonies. The distributed design responds to the particular limitations of an emergency situation; mainly, the fixed infrastructures are out of service because no energy supply is available. The implementation is based on the VANET facilities complemented with passive RFID tags or GPS localization. The vehicles can use the information of previous vehicles to dynamically decide the best path. A scale prototype has been developed to validate the system. It consists of several small size robotic vehicles, a test road circuit and a visual monitorization system. The security of the system is provided by a combination of data aggregation and reputation lists.Proyecto TIN 2011-25452 (TUERI: Technologies for secUre and Efficient wiReless networks within the Internet of things with applications in transport and logistic). Y Universidad de Málaga-Campus de Excelencia Internacional Andalucia Tech

Configuración Software de la directividad de arrays lineales

A line array can be defined as a column of loudspeakers that is designed so that these work together to achieve a higher directivity. This paper presents an application that enables a user to rotate the wavefront of uniform line arrays. Theoretical background and details of the implementation are provided. The validity of the application is tested with measurements of the directivity that are also compared with simulations.Este trabajo ha sido financiado Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech y el grupo de investigación Aplicación de las Tecnologías de la Información y Comunicaciones (PAI TIC-208)

RFID ownership transfer with positive secrecy capacity channels

RFID ownership transfer protocols (OTPs) transfer tag ownership rights. Recently, there has been considerable interest in such protocols, however, guaranteeing privacy for symmetric-key settings without trusted third parties (TTPs) is a challenge still unresolved. In this paper, we address this issue and show that it can be solved by using channels with positive secrecy capacity. We implement these channels with noisy tags and provide practical values, thus proving that perfect secrecy is theoretically possible. We then define a communication model that captures spatiotemporal events and describe a first example of symmetric-key based OTP that: (i) is formally secure in the proposed communication model and (ii) achieves privacy with a noisy tag wiretap channel without TTPs

Attacks on Secure Ownership Transfer for Multi-Tag Multi-Owner Passive RFID Environments

Sundaresan et al proposed recently a novel ownership transfer protocol for multi-tag multi-owner RFID environments that complies with the EPC Class1 Generation2 standard. The authors claim that this provides individual-owner privacy and prevents tracking attacks. In this paper we show that this protocol falls short of its security objectives. We describe attacks that allow: a) an eavesdropper to trace a tag, b) the previous owner to obtain the private information that the tag shares with the new owner, and c) an adversary that has access to the data stored on a tag to link this tag to previous interrogations (forward-secrecy). We then analyze the security proof and show that while the first two cases can be solved with a more careful design, for lightweight RFID applications strong privacy remains an open problem

Ensembles of Deep Learning Architectures for the Early Diagnosis of the Alzheimer’s Disease.

Computer Aided Diagnosis (CAD) constitutes an important tool for the early diagnosis of Alzheimer’s Disease (AD), which, in turn, allows the application of treatments that can be simpler and more likely to be effective. This paper explores the construction of classification methods based on deep learning architectures applied on brain regions defined by the Automated Anatomical Labeling (AAL). Gray Matter (GM) images from each brain area have been split into 3D patches according to the regions defined by the AAL atlas and these patches are used to train different deep belief networks. An ensemble of deep belief networks is then composed where the final prediction is determined by a voting scheme. Two deep learning based structures and four different voting schemes are implemented and compared, giving as a result a potent classification architecture where discriminative features are computed in an unsupervised fashion. The resulting method has been evaluated using a large dataset from the Alzheimer’s disease Neuroimaging Initiative (ADNI). Classification results assessed by cross-validation prove that the proposed method is not only valid for differentiate between controls (NC) and AD images, but it also provides good performances when tested for the more challenging case of classifying Mild Cognitive Impairment (MCI) Subjects. In particular, the classification architecture provides accuracy values up to 0.90 and AUC of 0.95 for NC/AD classification, 0.84 and AUC of 0.91 for stable MCI/AD classification and 0.83 and AUC of 0.95 for NC/MCI converters classification.This work was partly supported by the MICINN un der the projects TEC2012-34306 and PSI2015-65848- R, and the Consejer´ıa de Innovaci´on, Ciencia y Em presa (Junta de Andaluc´ıa, Spain) under the Ex cellence Projects P09-TIC-4530, P11-TIC-7103 and the Universidad de M´alaga. Programa de fortalec imiento de las capacidades de I+D+I en las Uni versidades 2014-2015, de la Consejer´ıa de Econom´ıa, Innovaci´on, Ciencia y Empleo, cofinanciado por el fondo europeo de desarrollo regional (FEDER) un der the project FC14-SAF30. Data collection and sharing for this project was funded by the Alzheimer’s Disease Neuroimaging Ini tiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bio engineering, and through generous contributions from the following: AbbVie, Alzheimer’s Associa tion; Alzheimer’s Drug Discovery Foundation; Ara clon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; Eu roImmun; F. Hoffmann-La Roche Ltd and its affili ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.;Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity ; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Re search is providing funds to support ADNI clinical sites in Canada. Private sector contributions are fa cilitated by the Foundation for the National Insti tutes of Health (www.fnih.org). The grantee organi zation is the Northern California Institute for Re search and Education, and the study is coordinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California

An enhanced symmetric-key based 5G-AKA protocol

5G technology is called to support the next generation of wireless communications and realize the “Internet of Everything” through its mMTC (massive Machine-Type-Communications) service. The recently standardized 5G-AKA protocol is intended to deal with security and privacy issues detected in earlier generations. Nevertheless, several 5G-AKA shortcomings have been reported, including a possibly excessive computational complexity for many IoT devices. To address these, a promising lightweight 2-pass authentication and key agreement (AKA) protocol for 5G mobile communications has recently been proposed by Braeken. Compared to the 5G-AKA protocol, this does not require the use of public key encryption. This paper analyzes the security claims of Braeken’s protocol and shows that it does not provide full unlinkability, but only session unlinkability, and is (still) subject to Linkability of AKA Failure Messages (LFM) attacks. We propose solutions to such problems and prove that symmetric-key based protocols cannot offer higher privacy protection levels without compromising availability. We then describe an enhanced version of this protocol that addresses these vulnerabilities and supports forward secrecy, which is a desirable feature for low-cost IoT devices.This work was supported in part by Funding for open access charge: Universidad de Málaga/CBUA, FEDER funds (Junta de Andalucía-University of Málaga) under Project UMA18-FEDERJA-172 and by Junta de Andalucía and ERDF under Project UMA-CEIATECH-11, and in part by NSF, USA under Grant 1565215

Hybrid genetic algorithm for clustering IC topographies of EEGs

Clustering of independent component (IC) topographies of Electroencephalograms (EEG) is an effective way to find brain-generated IC processes associated with a population of interest, particularly for those cases where event-related potential features are not available. This paper proposes a novel algorithm for the clustering of these IC topographies and compares its results with the most currently used clustering algorithms. In this study, 32-electrode EEG signals were recorded at a sampling rate of 500 Hz for 48 participants. EEG signals were pre-processed and IC topographies computed using the AMICA algorithm. The algorithm implements a hybrid approach where genetic algorithms are used to compute more accurate versions of the centroids and the final clusters after a pre-clustering phase based on spectral clustering. The algorithm automatically selects the optimum number of clusters by using a fitness function that involves local-density along with compactness and separation criteria. Specific internal validation metrics adapted to the use of the absolute correlation coefficient as the similarity measure are defined for the benchmarking process. Assessed results across different ICA decompositions and groups of subjects show that the proposed clustering algorithm significantly outperforms the (baseline) clustering algorithms provided by the software EEGLAB, including CORRMAP.Funding for open access charge: Universidad de Málaga / CBUA Funding for open access publishing: Universidad Málaga/CBUA. This work was supported by projects PGC2018-098,813-B C32 (Spanish “Ministerio de Ciencia, Innovación y Universidades”), UMA20-FEDERJA-086 (Consejería de economía y conocimiento, Junta de Andalucía), Project P18-rt-1624, and by European Regional Development Funds (ERDF). We also thank the Leeduca research group and Junta de Andalucía for the data supplied and the support

Empirical Functional PCA for 3D Image Feature Extraction Through Fractal Sampling.

Medical image classification is currently a challenging task that can be used to aid the diagnosis of different brain diseases. Thus, exploratory and discriminative analysis techniques aiming to obtain representative features from the images play a decisive role in the design of effective Computer Aided Diagnosis (CAD) systems, which is especially important in the early diagnosis of dementia. In this work, we present a technique that allows using specific time series analysis techniques with 3D images. This is achieved by sampling the image using a fractal-based method which preserves the spatial relationship among voxels. In addition, a method called Empirical functional PCA (EfPCA) is presented, which combines Empirical Mode Decomposition (EMD) with functional PCA to express an image in the space spanned by a basis of empirical functions, instead of using components computed by a predefined basis as in Fourier or Wavelet analysis. The devised technique has been used to classify images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Parkinson Progression Markers Initiative (PPMI), achieving accuracies up to 93% and 92% differential diagnosis tasks (AD versus controls and PD versus Controls, respectively). The results obtained validate the method, proving that the information retrieved by our methodology is significantly linked to the diseases.This work was partly supported by the MINECO/ FEDER under TEC2015-64718-R and PSI2015- 65848-R projects and the Consejer´ıa de Innovaci´on, Ciencia y Empresa (Junta de Andaluc´ıa, Spain) under the Excellence Project P11-TIC-7103 as well as the Salvador deMadariaga Mobility Grants 2017. Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Depart ment of Defense award number W81XWH-12-2- 0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contribu tions from the following: AbbVie, Alzheimer’s Asso ciation; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol Myer Squibb Company; CereSpir, Inc.; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Ho mann-La Roche Ltd and its ali ated company Genentech, Inc.; Fujirebio; GE Health care; IXICO Ltd.; Janssen Alzheimer Immunother apy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; P zer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clin ical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coor dinated by the Alzheimer’s Disease Cooperative Study at the University of California, San Diego. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern Cali fornia. PPMI a public-private partnership is funded by the Michael J. Fox Foundation for Parkinson’s Research and funding partners, including [list the full names of all of the PPMI funding partners found at www.ppmi-info.org/fundingpartners]