Biblio

Cloud computing is widely deployed to handle challenges such as big data processing and storage. Due to the outsourcing and sharing feature of cloud computing, security is one of the main concerns that hinders the end users to shift their businesses to the cloud. A lot of cryptographic techniques have been proposed to alleviate the data security issues in cloud computing, but most of these works focus on solving a specific security problem such as data sharing, comparison, searching, etc. At the same time, little efforts have been done on program security and formalization of the security requirements in the context of cloud computing. We propose a formal definition of the security of cloud computing, which captures the essence of the security requirements of both data and program. Analysis of some existing technologies under the proposed definition shows the effectiveness of the definition. We also give a simple look-up table based solution for secure cloud computing which satisfies the given definition. As FPGA uses look-up table as its main computation component, it is a suitable hardware platform for the proposed secure cloud computing scheme. So we use FPGAs to implement the proposed solution for k-means clustering algorithm, which shows the effectiveness of the proposed solution.
 

With the urban traffic planning and management development, it is a highly considerable issue to analyze and estimate the original-destination data in the city. Traditional method to acquire the OD information usually uses household survey, which is inefficient and expensive. In this paper, the new methodology proposed that using mobile phone data to analyze the mechanism of trip generation, trip attraction and the OD information. The mobile phone data acquisition is introduced. A pilot study is implemented on Beijing by using the new method. And, much important traffic information can be extracted from the mobile phone data. We use the K-means clustering algorithm to divide the traffic zone. The attribution of traffic zone is identified using the mobile phone data. Then the OD distribution and the commuting travel are analyzed. At last, an experiment is done to verify availability of the mobile phone data, that analyzing the "Traffic tide phenomenon" in Beijing. The results of the experiments in this paper show a great correspondence to the actual situation. The validated results reveal the mobile phone data has tremendous potential on OD analysis.