Background:
Big data analytics is the process of utilizing a collection of data accompanied
on the internet to store and retrieve anywhere and at any time. Big data is not simply a data but
it involves the data generated by variety of gadgets or devices or applications.
Objective:
When massive volume of data is stored, there is a possibility for malevolent attacks on
the searching data are stored in the server because of under privileged privacy preserving approaches.
These traditional methods result in many drawbacks due to various attacks on sensitive information.
Hence, to enhance the privacy preserving for sensitive information stored in the database,
the proposed method makes use of efficient methods.
Methods:
In this manuscript, an optimal privacy preserving over big data using Hadoop and mapreduce
framework is proposed. Initially, the input data is grouped by modified fuzzy c means clustering
algorithm. Then we are performing a map reduce framework. And then the clustered data is
fed to the mapper; in mapper the privacy of input data is done by convolution process. To validate
the privacy of input data the recommended technique utilizes the optimal artificial neural network.
Here, oppositional fruit fly algorithm is used to enhancing the neural networks.
Results:
The routine of the suggested system is assessed by means of clustering accuracy, error value,
memory, and time. The experimentation is performed by KDD dataset.
Conclusion:
A result shows that our proposed system has maximum accuracy and attains the effective
convolution process to improve privacy preserving.
Privacy Preserving Unstructured Big Data Analytics: Issues and Challenges