Secretly Pruned Convolutional Codes: Security Analysis and Performance Results PROJECT TITLE :Secretly Pruned Convolutional Codes: Security Analysis and Performance ResultsABSTRACT:Constructions of secure channel encoders, based on secret pruning, are considered during this paper. The key defines how pruning is applied on a mother convolutional code. This leads to a secret subspace that legitimate users are using to perform decoding, in contrast to an eavesdropper that employs the mother code. Each reliability and security aspects of the joint theme are treated. We tend to derive the expected weight enumerating operate of the secret subcode and show that the legitimate users achieve a higher performance (that depends on the pruning rate) in terms of word and bit error rate compared with the eavesdroppers. The protection depends on the notion of indistinguishability against chosen plaintext attacks. The security proofs are given within the random oracle model, and it is shown that a randomized version of the proposed joint scheme is semantically secure by relying on the hardness of the training parities with noise problem. The above-mentioned results are achieved by introducing a new model for physical encryption to consider the contribution of the channel noise to the system’s security. Did you like this research project? To get this research project Guidelines, Training and Code... Click Here facebook twitter google+ linkedin stumble pinterest Collective Travel Planning in Spatial Networks Affective Computing and Sentiment Analysis
