A novel data security framework using E-MOD for big data

With the development of various new technologies in the field of network technology and also cloud computing, there is also lots of data that is involved in all of it. Thus the concept of big data is introduced which is not an entirely new technology but an extension to Data Mining. Thus securing this Big Data is both important as well as challenging. Since the size of the data is very large it is not possible to secure the whole of big data. Therefore in this paper we are introducing a Big Data security mechanism wherein we will first select only certain attributes that has a higher value than the rest and secure them, which in turn provides security to the whole of Big Data. Since we are using a selection mechanism, the relevance between the attributes of a dataset is very important. Therefore we focus on two main things-Firstly, securing Big data through protecting valuable information inside. Secondly, it is impossible to protect all big data and its attributes. We consider big data as a single object which has its own attributes. We assume that a attribute which have a higher relevance is more important than other attributes. Then we focus on securing the data using the MOBAT technique. Now we propose a Data masking technique that will protect these attributes. Data masking is a technique in which we replace the original set of data with another set of data that is not real but realistic. The Big Data is masked using a mathematical formula that makes use of modulus operator. Thus through these techniques we are providing security to Big Data.