Showing papers by "M. Pallikonda Rajasekaran published in 2013"

An efficient hierarchical attribute set based encryption scheme with revocation for outsourcing personal health records in cloud computing

Abstract: Enterprises will outsource their sensitive data in a cloud server due to the rapid development of cloud computing in the IT industry for last few years. It is attractive for the Personal Health Record (PHR) service providers to shift their PHR applications and storage into the cloud. Under encryption, it is excited to achieve fine-grained access control to PHR data in a scalable and efficient way. It also includes the problem of establishing access control for the encrypted data, and revoking or withdrawing the access rights from users when they are no longer authorized to access the encrypted data on cloud servers. But the use of one single Trusted Authority (TA) and Cipher text Policy (CP-ABE) are unable to manage multiple group owners for encryption process and access policy. In order to realize scalability, flexibility, and fine-grained access control of outsourced data in cloud computing, we leverage Hierarchical Attribute-Set-Based Encryption (HASBE). This HASBE extends the cipher text-policy Attribute-Set-Based Encryption (ASBE) with a hierarchical structure of users by means of compound attributes. The intended scheme achieves fine-grained, flexible and scalable data access control with the help of compound attributes of HASBE.

Analysis of fuzzy segmented based reconstructed PET liver image using MLEM algorithm

Abstract: This paper possess the fuzzy segmented based performance analysis for the reconstruction of a non linear PET Liver image using fuzzy logic controlled Maximum Likelihood Expectation Maximization (MLEM). The various types of iteration method used are MAP, MLEM and OSEM. MLEM, an iteration approach, has more advantage for reconstructing the Positron Emission Tomography (PET) when comparing with the analytical approach which requires a minimization of a convex cost function and accompanied by many problems related to the computational complexity. Knowledge based fuzzy controlled technique is used which provides better Average Difference value along with lowest noise in the PET Liver image thus improves the using of Maximum Likelihood Expectation Maximization (MLEM) algorithm. The PET liver image is constructed and simulated in MATLAB/Simulink package.