Project Dissertation Report

Project Dissertation Report-73
In this thesis, we designed a group signature protocol based upon hard computa- tional assumptions such as, Discrete Logarithm Problem (DLP), Integer Factor- ization Problem (IFP), and Computational Die Hellmann (CDH) problem.The proposed scheme is proved to be resistant against colluding attack.

In this thesis, we designed a group signature protocol based upon hard computa- tional assumptions such as, Discrete Logarithm Problem (DLP), Integer Factor- ization Problem (IFP), and Computational Die Hellmann (CDH) problem.The proposed scheme is proved to be resistant against colluding attack.

This paper is a comparative study for educational purpose.

It provides analysis based on practical experiments carried out on a number of security solutions regarding their ability to detect ARP spoofing.

Any client can verify the authenticity of the document by using the public parameters of the group.

The identity of the group member cannot be revealed from the group signature.

We Provide computer science, Information Technology and Computer Engineering related projects for ME, MTech and MS students.

You can download your project from our readymade project list specifically collected for Master of Engineering(ME), Master Of Technology(MTech) and Master of Science(MS).As a solution, we propose the requirements for an ideal algorithm that can be used by security solutions to detect effectively any ARP spoofing attack.Analysis and interpretation of DNA Microarray data is a fundamental task in bioinformatics.It includes unexplored unique topics that we are collected form different online resources.We provide this Master degree project for your guide to complete your thesis or dissertation of Master of Engineering(ME), Master of Technology(MTech) and Master of Science(MS).First, we implement PCA for significant feature extraction and then FFNN trained using Backpropagation (BP) and SVM are implemented on the reduced feature set.Next, we propose a Multiobjective Genetic Algorithm-based fuzzy clustering technique using real coded encoding of cluster centers for clustering and clas- si cation.This enhances the speed and accuracy of the classifiers.Principal Component Analysis (PCA) is a technique used for feature extraction which helps to retrieve intrinsic information from high dimensional data in eigen spaces to solve the curse of dimensionality problem.Moreover, the group signature remains valid, if some members leave the group or some new mem- bers join the group.Full traceability feature is con rmed in the proposed scheme.

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