Securing Named Data Networking: Attribute-Based Encryption and Beyond

As one of the promising information-centric networking (ICN) architectures, NDN has attracted tremendous research attention and effort in the past decade. In particular, security and privacy remain significant concerns and challenges due to the fact that most of the traditional cryptographic primitives are no longer suitable for NDN architecture. For example, the traditional cryptographic primitives aim to secure point-to-point communications, always requiring explicit descriptions of to where or whom the data packets are intended, while network addressing or locating in NDN becomes implicit. To deal with such issues, the recently developed cryptographic primitives such as ABE have been applied to NDN. Also, to efficiently solve the trust-roots problem and seamlessly deploy cryptographic infrastructures, the concept of SDN has been introduced to NDN as well. This tutorial is devoted to exploring the interesting integration between NDN, ABE, and SDN

Effect of Sand Relative Density on Response of a Laterally Loaded Pile and Sand Deformation

Copyright © 2015 Bingxiang Yuan et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.Two scale-model tests were separately conducted in standard Toyoura sand with relative density of 50% and 80%. The effect of sand relative density on pile-soil interaction was investigated through the response of a laterally loaded pile and the sand movement around the pile. At a displacement of 3.6 mm of the loading point, the applied loads in loose and dense sand were 4.775 N and 21.025 N, respectively, and the maximum moment and soil resistance of the pile in dense sand were over 4 times those in loose sand. However, the deflection of the pile in dense sand was less than that in loose sand; additionally, the depth of zero deflection in dense sand was also less than that in loose sand. At the same time, the maximum displacements of loose sand in the vertical profile and ground surface were over 1.5 times those of dense sand. These characteristics occurred because the relative stiffness ratio of soil and pile increased as the relative density increased, which caused the behavior of the pile in dense sand to be elastic rather than rigid. In addition, the compacted sand particles did not move as easily as the loose sand particles

A Secure and Efficient Scalable Secret Image Sharing Scheme with Flexible Shadow Sizes.

In a general (k, n) scalable secret image sharing (SSIS) scheme, the secret image is shared by n participants and any k or more than k participants have the ability to reconstruct it. The scalability means that the amount of information in the reconstructed image scales in proportion to the number of the participants. In most existing SSIS schemes, the size of each image shadow is relatively large and the dealer does not has a flexible control strategy to adjust it to meet the demand of differen applications. Besides, almost all existing SSIS schemes are not applicable under noise circumstances. To address these deficiencies, in this paper we present a novel SSIS scheme based on a brand-new technique, called compressed sensing, which has been widely used in many fields such as image processing, wireless communication and medical imaging. Our scheme has the property of flexibility, which means that the dealer can achieve a compromise between the size of each shadow and the quality of the reconstructed image. In addition, our scheme has many other advantages, including smooth scalability, noise-resilient capability, and high security. The experimental results and the comparison with similar works demonstrate the feasibility and superiority of our scheme

Identification of Coupled Map Lattice Based on Compressed Sensing

A novel approach for the parameter identification of coupled map lattice (CML) based on compressed sensing is presented in this paper. We establish a meaningful connection between these two seemingly unrelated study topics and identify the weighted parameters using the relevant recovery algorithms in compressed sensing. Specifically, we first transform the parameter identification problem of CML into the sparse recovery problem of underdetermined linear system. In fact, compressed sensing provides a feasible method to solve underdetermined linear system if the sensing matrix satisfies some suitable conditions, such as restricted isometry property (RIP) and mutual coherence. Then we give a low bound on the mutual coherence of the coefficient matrix generated by the observed values of CML and also prove that it satisfies the RIP from a theoretical point of view. If the weighted vector of each element is sparse in the CML system, our proposed approach can recover all the weighted parameters using only about M samplings, which is far less than the number of the lattice elements N. Another important and significant advantage is that if the observed data are contaminated with some types of noises, our approach is still effective. In the simulations, we mainly show the effects of coupling parameter and noise on the recovery rate