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Top1. Introduction
Now a days, huge amount of information are generated through mobile phones, satellite, digital cameras and other sources. These massive information are not fully secured. With the growth of the technology, several algorithms are developed in order to provide security. Cryptography and Steganography (Choras, R. S. 2007). methods are used to encrypt the data as well as to hide the data in multimedia image. Cryptography is used to encrypt information that is converting information into cipher text. The human and the computer are unable to read this encrypted (Patil, S. M. 2012). information without the proper cipher to decrypt it. Strong ciphers should be difficult to break, that is inability on the part of the code breaker to break it in less time or by using limited computer resources. Steganography is a technique that is used to hide the information within another information. The information can be hidden in multimedia image also using LSB algorithm (Prakash, M., Sayeed, R. F., Princey, S., & Priyanka, S. 2015) in which we first do the scanning process in which it scan the image row by row and encode it in binary. Then encode the secret message in binary and check the size of the image and the size of the secret message. For each of the pixels of the image, choose one pixel of the image randomly and divide the image into four parts (Alpha, Red, Green and Blue parts) and hide two by two bits of the secret message in each part of the pixel in the two least significant bits. The encrypted data is embedded in the image so that it cannot be seen. It establishes a communication that is not directly visible by hiding information in an image (Annamalai, R., Srikanth, J. 2015). It provides a situation that an unauthorized person is unaware about the presence of hidden information and so they will be unable to access that original information.
The features can be extracted from that image on a distributed computing platform by using Apache Hadoop framework. With the rapid growth of technology and social media, lots of information in the form of text documents and multimedia is generating. To process such a huge amount of information is a big data (Sagiroglu, S., & Sinanc, D. 2013) problem in the present domain. As a solution to this big data problem Hadoop came up that almost solved this problem. By this approach memory is increased and execution time will be faster due the parallel execution of Hadoop cluster. Hadoop takes care of single point of failure, the system is highly error sophisticated and less susceptible to node failures. Hadoop is an open-source implementation of the Map Reduce platform and distributed file system (Katal, A., Wazid, M., & Goudar, R. H. 2013). The Hadoop system is written in Java. Hadoop Streaming is a utility that comes with the Hadoop distribution and that can be used to invoke streaming programs that are not written in Java (such as Ruby, Perl, Python, PHP, R, or C++). Using this utility, we can execute database programs written in the Ruby programming language. The utility also allows the user to create and run Map and Reduce jobs with any executable programs or scripts as the mapper and the reducer. An execution of a program when using Hadoop Streaming (Yamamoto, M., & Kaneko, K. 2012) and a description of the Map and Reduce functions in the Ruby programming language. Key-value pairs can be specified to depend on the input–output formats.
The Hadoop framework is one of the common frameworks that processes distributed big data by applying the MapReduce method. The Hadoop framework uses a distributed, scalable, and portable Hadoop Distributed File System (HDFS) (Liao, H., Han, J., & Fang, J. 2010), which is written in the Java programming language in order to perform these jobs (Patel, A. B., Birla, M., & Nair, U. 2012). HDFS (Zikopoulos, P., & Eaton, C. 2011) allows big data to form blocks, distributed to nodes, processed and retrieved successfully (Wu, Y., Ye, F., Chen, K., & Zheng, W. 2014).