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      Rateless Resilient Network Coding Against Byzantine Adversaries

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          Abstract

          This paper considers rateless network error correction codes for reliable multicast in the presence of adversarial errors. Most existing network error correction codes are designed for a given network capacity and maximum number of errors known a priori to the encoder and decoder. However, in certain practical settings it may be necessary to operate without such a priori knowledge. We present rateless coding schemes for two adversarial models, where the source sends more redundancy over time, until decoding succeeds. The first model assumes there is a secret channel between the source and the destination that the adversaries cannot overhear. The rate of the channel is negligible compared to the main network. In the second model, instead of a secret channel, the source and destination share random secrets independent of the input information. The amount of secret information required is negligible compared to the amount of information sent. Both schemes are optimal in that decoding succeeds with high probability when the total amount of information received by the sink satisfies the cut set bound with respect to the amount of message and error information. The schemes are distributed, polynomial-time and end-to-end in that other than the source and destination nodes, other intermediate nodes carry out classical random linear network coding.

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          Network information flow

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            A Random Linear Network Coding Approach to Multicast

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              Network Error Correction, II: Lower Bounds

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                Author and article information

                Journal
                2013-01-14
                Article
                1301.2860
                e7083e2d-dc24-4cee-80a5-9db40477717e

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

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                Custom metadata
                cs.IT math.IT

                Numerical methods,Information systems & theory
                Numerical methods, Information systems & theory

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