@article{MAKHILLIJSC201813421452,
    title = {A Big Data Security Method Using Modulus Operator},
    journal = {International Journal of Soft Computing},
    volume = {13},
    number = {4},
    pages = {123-128},
    year = {2018},
    issn = {1816-9503},
    doi = {ijscomp.2018.123.128},
    url = {https://makhillpublications.co/view-article.php?issn=1816-9503&doi=ijscomp.2018.123.128},
    author = {R.A.,Ravindra and},
    keywords = {Big data,data security,data masking,huge volume,utilization,India},
    abstract = {Due to internet of things and social media platforms, raw data is getting generated from systems
around us in three 60° with respect to time, volume and type. Social networking is increasing rapidly to exploit
business advertisements as business demands. In this regard, there are many challenges for data management
service providers, security is one among them. Data management service providers need to ensure security for
their privileged customers in providing accurate and valid data. Since, underlying transactional data have
varying data characteristics such huge volume, variety and complexity, there is an essence of deploying such
data sets on to the big data platforms which can handle structured, semi-structured and un-structured data sets.
In this regard we propose a data masking technique for big data security. Data masking ensures proxy of
original dataset with a different dataset which is not real but looks realistic. The given data set is masked using
modulus operator and the concept of keys. Our experiment advocates enhanced modulus based data masking
is better with respect to execution time and space utilization for larger data sets when compared to modulus
based data masking. This research will help big data developers, quality analysts in the business domains and
provides confidence for end-users in providing data security.}
    }