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A new parallel processing architecture for accelerating image encryption based on chaos
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DOI：10.1016/j.jisa.2021.103056.png)
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This study introduces a novel parallel processing architecture for accelerating image encryption based on chaos. In the proposed architecture, whole image data is split into partitions of particular size to create separate encryption threads. As the proposed cryptosystem employs several identical chaotic ciphers running concurrently and independently to process the partitions, it greatly leverages the degree of parallelism to some extent. A powerful output mixing logic based on an additional chaotic function, and simple exclusive-OR and shift op-erations is innovatively incorporated to ensure inter-partition diffusion. Since there is no dependency on previous data bytes in the introduced logic, blending operations applied on the outputs of independent encryption threads can be concurrently executed by exploiting loop-level parallelism to the extent allowed by data processing units available. The number of blending operations that should be carried out for an image is kept proportional to the partition size which also directly determines the number of separate encryption threads created. In order to measure encryption/decryption runtimes, the proposed architecture has been tested on two different multi-core CPUs, namely 4-core and 8-core. The obtained results show that the proposed cryptosystem parallelising sequential operations by introducing a multi-threaded encryption architecture is much faster than the base ci-pher and most of the other state-of-the-art algorithms. Having successfully passed various security tests, the proposed cryptosystem manifests its robustness against cryptographic attacks, and hence become evident that it is efficient for secure transmission.
Keyword:
Chaos theory
Chaotic image encryption
Logistic map
Parallelization
Multithread
Security
期刊
IF:
3.7
论文数:
1.9K
被引数:
4.9K
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A novel image encryption algorithm based on polynomial combination of chaotic maps and dynamic function generation.png)
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