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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Indexing

Abstract

AN OPTIMAL CHANNEL PREDICTION OF PSEASMC MODEL

*S. Samson Dinakaran and Dr. M. Devapriya

ABSTRACT

In the previous researches, Sublinear communication and Quantum protocol in Performance and Security Enhanced Asynchronous Secure Multiparty Computation (SQPSEASMC) model has been designed to reduce both storage and communication costs. In this model, the upper bounds of multiparty quantum channels were represented in terms of multiparty squashed entanglement and derived on the rates at which GHz channels or multiparty secret channels which can be distributed among a number of parties over an arbitrary quantum broadcast network. On the other hand, the open issue was that whether bounds can also be obtained for a network of multiple access channels which were normally noisy non-local gates. For this reason, an aggregating quantum repeater was presented that derives the upper bound to restrict the quantum and secret abilities over the network. Still, the open issue is that whether the optimality of the aggregated quantum repeaters satisfies the relationship between the quantum channels and the number of links between Alice and Bob in deriving entanglement. Hence in this paper, Sublinear communication and Quantum protocol with Channel Prediction in Performance and Security Enhanced ASMC (SQPSECP-ASMC) is proposed. In this model, the optimality of aggregated quantum repeaters is achieved by using a supervised rank aggregation method that predicts the unconnected links in a SMC networks. At first, different topological measures are used to rank a list of unlinked nodes in a network at a given time. After that, the new links at a consecutive time interval is predicted based on the weighted value of each topological measure. Further, the supervised rank aggregation model for link prediction is built by using these learned weights. Finally, the experimental results show that the proposed SQPSECP-ASMC model achieves better performance than the SQPSEASMC in terms of differential privacy, latency and accuracy.

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