Accelerating ID-based Encryption based on Trapdoor DL using Pre-computation

The existing identity-based encryption (IBE) schemes based on pairings require pairing computations in encryption or decryption algorithm and it is a burden to each entity which has restricted computing resources in mobile computing environments. An IBE scheme (MY-IBE) based on a trapdoor DL group for RSA setting is one of good alternatives for applying to mobile computing environments. However, it has a drawback for practical use, that the key generation algorithm spends a long time for generating a user\u27s private key since the key generation center has to solve a discrete logarithm problem. In this paper, we suggest a method to reduce the key generation time of the MY-IBE scheme, applying modified Pollard rho algorithm using significant pre-computation (mPAP). We also provide a rigorous analysis of the mPAP for more precise estimation of the key generation time and consider the parallelization and applying the tag tracing technique to reduce the wall-clock running time of the key generation algorithm. Finally, we give a parameter setup method for an efficient key generation algorithm and estimate key generation time for practical parameters from our theoretical analysis and experimental results on small parameters. Our estimation shows that it takes about two minutes using pre-computation for about 50 days with 27 GB storage to generate one user\u27s private key using the parallelized mPAP enhanced by the tag tracing technique with 100 processors

The Private Key Capacity of a Cooperative Pairwise-Independent Network

This paper studies the private key generation of a cooperative pairwise-independent network (PIN) with M+2 terminals (Alice, Bob and M relays), M >= 2. In this PIN, the correlated sources observed by every pair of terminals are independent of those sources observed by any other pair of terminal. All the terminals can communicate with each other over a public channel which is also observed by Eve noiselessly. The objective is to generate a private key between Alice and Bob under the help of the M relays; such a private key needs to be protected not only from Eve but also from individual relays simultaneously. The private key capacity of this PIN model is established, whose lower bound is obtained by proposing a novel random binning (RB) based key generation algorithm, and the upper bound is obtained based on the construction of M enhanced source models. The two bounds are shown to be exactly the same. Then, we consider a cooperative wireless network and use the estimates of fading channels to generate private keys. It has been shown that the proposed RB-based algorithm can achieve a multiplexing gain M-1, an improvement in comparison with the existing XOR- based algorithm whose achievable multiplexing gain is about [M]/2.Comment: 5 pages, 3 figures, IEEE ISIT 2015 (to appear

Knowledge Graph semantic enhancement of input data for improving AI

Intelligent systems designed using machine learning algorithms require a large number of labeled data. Background knowledge provides complementary, real world factual information that can augment the limited labeled data to train a machine learning algorithm. The term Knowledge Graph (KG) is in vogue as for many practical applications, it is convenient and useful to organize this background knowledge in the form of a graph. Recent academic research and implemented industrial intelligent systems have shown promising performance for machine learning algorithms that combine training data with a knowledge graph. In this article, we discuss the use of relevant KGs to enhance input data for two applications that use machine learning -- recommendation and community detection. The KG improves both accuracy and explainability

Environment identification based memory scheme for estimation of distribution algorithms in dynamic environments

Copyright @ Springer-Verlag 2010.In estimation of distribution algorithms (EDAs), the joint probability distribution of high-performance solutions is presented by a probability model. This means that the priority search areas of the solution space are characterized by the probability model. From this point of view, an environment identification-based memory management scheme (EI-MMS) is proposed to adapt binary-coded EDAs to solve dynamic optimization problems (DOPs). Within this scheme, the probability models that characterize the search space of the changing environment are stored and retrieved to adapt EDAs according to environmental changes. A diversity loss correction scheme and a boundary correction scheme are combined to counteract the diversity loss during the static evolutionary process of each environment. Experimental results show the validity of the EI-MMS and indicate that the EI-MMS can be applied to any binary-coded EDAs. In comparison with three state-of-the-art algorithms, the univariate marginal distribution algorithm (UMDA) using the EI-MMS performs better when solving three decomposable DOPs. In order to understand the EI-MMS more deeply, the sensitivity analysis of parameters is also carried out in this paper.This work was supported by the National Nature Science Foundation of China (NSFC) under Grant 60774064, the Engineering and Physical Sciences Research Council (EPSRC) of UK under Grant EP/E060722/01

Distributed video coding for wireless video sensor networks: a review of the state-of-the-art architectures

Distributed video coding (DVC) is a relatively new video coding architecture originated from two fundamental theorems namely, Slepian–Wolf and Wyner–Ziv. Recent research developments have made DVC attractive for applications in the emerging domain of wireless video sensor networks (WVSNs). This paper reviews the state-of-the-art DVC architectures with a focus on understanding their opportunities and gaps in addressing the operational requirements and application needs of WVSNs

Population-based incremental learning with associative memory for dynamic environments

Copyright © 2007 IEEE. Reprinted from IEEE Transactions on Evolutionary Computation. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In recent years there has been a growing interest in studying evolutionary algorithms (EAs) for dynamic optimization problems (DOPs) due to its importance in real world applications. Several approaches, such as the memory and multiple population schemes, have been developed for EAs to address dynamic problems. This paper investigates the application of the memory scheme for population-based incremental learning (PBIL) algorithms, a class of EAs, for DOPss. A PBIL-specific associative memory scheme, which stores best solutions as well as corresponding environmental information in the memory, is investigated to improve its adaptability in dynamic environments. In this paper, the interactions between the memory scheme and random immigrants, multi-population, and restart schemes for PBILs in dynamic environments are investigated. In order to better test the performance of memory schemes for PBILs and other EAs in dynamic environments, this paper also proposes a dynamic environment generator that can systematically generate dynamic environments of different difficulty with respect to memory schemes. Using this generator a series of dynamic environments are generated and experiments are carried out to compare the performance of investigated algorithms. The experimental results show that the proposed memory scheme is efficient for PBILs in dynamic environments and also indicate that different interactions exist between the memory scheme and random immigrants, multi-population schemes for PBILs in different dynamic environments

A-MAKE: an efficient, anonymous and accountable authentication framework for WMNs

In this paper, we propose a framework, named as A-MAKE, which efficiently provides security, privacy, and accountability for communications in wireless mesh networks. More specifically, the framework provides an anonymous mutual authentication protocol whereby legitimate users can connect to network from anywhere without being identified or tracked. No single party (e.g., network operator) can violate the privacy of a user, which is provided in our framework in the strongest sense. Our framework utilizes group signatures, where the private key and the credentials of the users are generated through a secure three-party protocol. User accountability is implemented via user revocation protocol that can be executed by two semitrusted authorities, one of which is the network operator. The assumptions about the trust level of the network operator are relaxed. Our framework makes use of much more efficient signature generation and verification algorithms in terms of computation complexity than their counterparts in literature, where signature size is comparable to the shortest signatures proposed for similar purposes so far