An algebraic approach to network coding
IEEE/ACM Transactions on Networking (TON)
On the Practical and Security Issues of Batch Content Distribution Via Network Coding
ICNP '06 Proceedings of the Proceedings of the 2006 IEEE International Conference on Network Protocols
Optimization principles and application performance evaluation of a multithreaded GPU using CUDA
Proceedings of the 13th ACM SIGPLAN Symposium on Principles and practice of parallel programming
Larrabee: a many-core x86 architecture for visual computing
ACM SIGGRAPH 2008 papers
Symbol-level network coding for wireless mesh networks
Proceedings of the ACM SIGCOMM 2008 conference on Data communication
Fast exponentiation with precomputation
EUROCRYPT'92 Proceedings of the 11th annual international conference on Theory and application of cryptographic techniques
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Tsunami: massively parallel homomorphic hashing on many-core GPUs
Concurrency and Computation: Practice & Experience
Speeding up k-Means algorithm by GPUs
Journal of Computer and System Sciences
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Recently, random linear network coding has been widely applied in peer-to-peer network applications. Instead of sharing the raw data with each other, peers in the network produce and send encoded data to each other. As a result, the communication protocols have been greatly simplified, and the applications experience higher end-to-end throughput and better robustness to network churns.Since it is difficult to verify the integrity of the encoded data, such systems can suffer from the famous pollution attack, in which a malicious node can send bad encoded blocks that consist of bogus data. Consequently, the bogus data will be propagated into the whole network at an exponential rate. Homomorphic hash functions (HHFs) have been designed to defend systems from such pollution attacks, but with a new challenge: HHFs require that network coding must be performed in GF(q ), where q is a very large prime number. This greatly increases the computational cost of network coding, in addition to the already computational expensive HHFs. This paper exploits the potential of the huge computing power of Graphic Processing Units (GPUs) to reduce the computational cost of network coding and homomorphic hashing. With our network coding and HHF implementation on GPU, we observed significant computational speedup in comparison with the best CPU implementation. This implementation can lead to a practical solution for defending against the pollution attacks in distributed systems.