Recently, Cloud has become quite attractive due to its elasticity, availability, and scalability. However, the technologies such as virtualization build up Cloud appear like a double-edged sword because of the expansion on attacking surfaces to entire hardware-software stack. Moreover, homogeneous computing in Cloud severely limits the computational power it could potentially provide. As a result, it is strongly desired to have new and comprehensive solutions to take in all benefits from Cloud and suppress backsides. This thesis proposes three new solutions to address security, computation and data issues in Cloud. Firstly, a GPU MapReduce framework specifically aims at improving performance and reducing energy consumption to data parallel problems in Cloud. In addition, the P-CP-ABE scheme overcomes not only the difficulties of data security, access control, and key management issues in Cloud, but the performance weakness of original CP-ABE is enhanced dramatically as well. Finally, the multi tenancy technology on top of the insecure network requires a strong network authentication protocol suite to assure authenticity and nonrepudiation in the Cloud.
|Commitee:||Kim, Donghoon, Su, Hung-Chi|
|School:||Arkansas State University|
|School Location:||United States -- Arkansas|
|Source:||MAI 56/05M(E), Masters Abstracts International|
|Subjects:||Information Technology, Computer science|
|Keywords:||ABE, Authentication, Computation, GPU, MapReduce, Security|
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