A data encryption model based on intertwining logistic map

Abstract A data encryption model is developed using Brownian Motion based confusion and intertwining logistic map based diffusion method. In the existing encryption models, shuffling is irrelevant for the pixels with same value i.e. (all white/black/same colour) and models are prone to differential and various statistical attacks. This article investigates random data insertion to ensure the validity of shuffling engine and result in a different cipher in each execution. It makes both differential and chosen plaintext attacks infeasible and reduces the correlation among existing image pixels. Further, an intertwining logistic map is used not only for better random number distribution but also to overcome the blank window noticed in the bifurcation diagram of logistic map. Brownian motion based confusion introduced key sensitivity in the encryption model. In last, Intertwining logistic map based diffusion model is applied so that even a single bit change affects most of the pixels in the cipher. Number of pixel change rate (NPCR) and Unified average change intensity (UACI) scores support the security enhancement of the model. Randomness is supported by the NIST randomness test. Simulation results show that the model has better security level, lossless compression, resistive against chosen and known plaintext attacks.

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