Steganography Encoding as Inverse Data Mining

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Author(s): Dan Ophir (Ariel University, Israel & Tel Aviv University, Israel)
Copyright: 2015
Pages: 24
Source title: Research and Applications in Global Supercomputing
Source Author(s)/Editor(s): Richard S. Segall (Arkansas State University, USA), Jeffrey S. Cook (Independent Researcher, USA)and Qingyu Zhang (Shenzhen University, China)
DOI: 10.4018/978-1-4666-7461-5.ch011

Keywords: Computer Science & IT / Grid & High Performance Computing / High Performance Computing / Information Science Reference

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Abstract

Supercomputers and cloud computing seem to be competing paradigms. Supercomputing focuses on increasing CPU speed, thus significantly increasing the speed of its associated memory access and its capacity. Conversely, cloud computing increases the computing throughput by parallel computing, spreading computing tasks over unused nodes and platforms. Steganography, the art of concealing a message within a message, is a type of encoding whose operations are required to remain secret. Steganography encoding requires data manipulation and is linked to data mining methodologies. Data mining reveals concealed data that is embedded in exposed data. Encoding by steganography is reverse data mining, hiding data among visible data. Conventionally, encryption methods are used to successfully hide the data. Cloud computing can take the data and disperse it in a way that even without any encryption, each individual packet of data is meaningless, thus hiding the message as like by steganography. This chapter explores steganography encoding as inverse data mining.

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