An improved chaotic image encryption algorithm using Hadoop-based MapReduce framework for massive remote sensed images in parallel IoT applications

Image encryption algorithms based on Chaotic approach are becoming increasingly popular for remotely sensed images using parallel techniques. It has been demonstrated that the most efficient image encryption algorithms are based on Chaos. Previous research using chaos-based cryptosystems has resulted in poor performance when using a single computer, compromising privacy, security, and reliability. Furthermore, there were issues when vulnerable satellite images were processed. This paper describes a novel chaos-based encryption technique that employs an external secret key and Henon, Logistic, and Gauss iterated maps. The proposed encryption algorithm is capable of efficiently encrypting a large number of images. When the number of images increases, however, these images become very small, and the technology becomes inefficient or impractical. This paper investigates the parallel method of image encryption on a large number of remotely sensed images in Hadoop. Hadoop's file visit method has been enhanced so that it can treat the entire Tiff file as a single unit. Furthermore, the file format is being extended to be supported by Hadoop in order to support GeoTiff in Hadoop. The results of the experiments show that the proposed parallel method for encryption is effective and scalable to a large number of images when compared to other well-known methods.