SECURE AND EFFICIENT DECENTRALIZED GROUP KEY ESTABLISHMENT REVISED ELGAMAL PROTOCOL FOR GROUP COMMUNICATION

in distributed system it is sometimes necessary for users to share the power to use a cryptosystem. The system secret is divided up into shares and securely stored by the entities forming the distributed cryptosystem. We propose a new Multi signature scheme without a trusted third party (TTP), based on a round optimal, publicly verifiable distributed key generation (DKG) protocol. In this propose system, we define a new propose ElGamal algorithm, in that ElGamal algorithm has two random numbers. The origina l ElGamal algorithm is that, it has only one random number. In order to improve its security, the proposed scheme adds one more random number. The security of the proposed signature scheme is the same with the ElGamal sig nature scheme which is based on the difficult computable nature of discrete logarithm over finite fields. In this paper, the algorithm is proposed to enhance the security and usage of more random number to make algorithm more complicate d, which can also make the link between the random number and the key more complicated. The scheme presented in this paper after analysis showed that the security level is kept high by using two random numbers and the time complex ity is reduced

Centric Model Assessment for Collaborative Data Mining

Data mining is the task of discovering interesting patterns from large amounts of data. There are many data mining tasks, such as classification, clustering, association rule mining and sequential pattern mining. Sequential pattern mining finds sets of data items that occur together frequently in some sequences. Collaborative data mining refers to a data mining setting where different groups are geographically dispersed but work together on the same problem in a collaborative way. Such a setting requires adequate software support. Group work is widespread in education. The goal is to enable the groups and their facilitators to see relevant as pects of the groups operation and provide feedbacks if these are more likely to be associated with positive or negative outcomes and where the problems are. We explore how useful mirror information can be extracted via a theory-driven approach and a range of clustering and sequential pattern mining. In this paper we describe an experiment with a simple implementation of such a collaborative data mining environment. The experiment brings to light several problems, one of which is related to model assessment. We discuss several possible solutions. This discussion can contribute to a better understanding of how collaborative data mining is best organized