Achieving Secure and Efficient Cloud Search Services: Cross-Lingual Multi-Keyword Rank Search over Encrypted Cloud Data

Multi-user multi-keyword ranked search scheme in arbitrary language is a novel multi-keyword rank searchable encryption (MRSE) framework based on Paillier Cryptosystem with Threshold Decryption (PCTD). Compared to previous MRSE schemes constructed based on the k-nearest neighbor searcha-ble encryption (KNN-SE) algorithm, it can mitigate some draw-backs and achieve better performance in terms of functionality and efficiency. Additionally, it does not require a predefined keyword set and support keywords in arbitrary languages. However, due to the pattern of exact matching of keywords in the new MRSE scheme, multilingual search is limited to each language and cannot be searched across languages. In this pa-per, we propose a cross-lingual multi-keyword rank search (CLRSE) scheme which eliminates the barrier of languages and achieves semantic extension with using the Open Multilingual Wordnet. Our CLRSE scheme also realizes intelligent and per-sonalized search through flexible keyword and language prefer-ence settings. We evaluate the performance of our scheme in terms of security, functionality, precision and efficiency, via extensive experiments

A Characterization for Compact Sets in the Space of Fuzzy Star-Shaped Numbers with L

By means of some auxiliary lemmas, we obtain a characterization of compact subsets in the space of all fuzzy star-shaped numbers with Lp metric for 1≤p<∞. The result further completes and develops the previous characterization of compact subsets given by Wu and Zhao in 2008

Sensing Task Allocation for Heterogeneous Channels in Cooperative Spectrum Sensing

In the traditional centralized cooperative spectrum sensing, all secondary users sense the same channel. But, for a given channel, there exists detection performance diversity among all the users, due to the different signal-fading process. Involving the user with poor performance in cooperative sensing will not only deteriorate the detection correctness but also waste the sensing time. In the heterogeneous channels, the problem is even severe. A novel idea is to allocate the secondary users to sense different channels. We analyze the allocation problem before formulate it to be an optimization problem, which is a NP-hard problem. Then we propose the declined complexity algorithm in equal secondary user case and the two-hierarchy approach algorithm in unequal case. With the simulation, we verify the near optimality of the proposed algorithms and the advantage of the task allocation

Big Data Analysis-Based Secure Cluster Management for Optimized Control Plane in Software-Defined Networks

In software-defined networks (SDNs), the abstracted control plane is its symbolic characteristic, whose core component is the software-based controller. The control plane is logically centralized, but the controllers can be physically distributed and composed of multiple nodes. To meet the service management requirements of large-scale network scenarios, the control plane is usually implemented in the form of distributed controller clusters. Cluster management technology monitors all types of events and must maintain a consistent global network status, which usually leads to big data in SDNs. Simultaneously, the cluster security is an open issue because of the programmable and dynamic features of SDNs. To address the above challenges, this paper proposes a big data analysis-based secure cluster management architecture for the optimized control plane. A security authentication scheme is proposed for cluster management. Moreover, we propose an ant colony optimization approach that enables big data analysis scheme and the implementation system that optimizes the control plane. Simulations and comparisons show the feasibility and efficiency of the proposed scheme. The proposed scheme is significant in improving the security and efficiency SDN control plane