Big data is a collection of large amount of data and such data that people use for improving the various customer care services. However, the data collected have posed many new problems for the scientists with regards to security and privacy. Hence, we have made a list of top ten security and privacy challenges that makes it difficult to use big data tools.
1) Security of data transactions
The data and logs that we store have more than one tier, but this is not enough. The companies have to secure these logs and data against unauthorized access. The availability of data is also very necessary.
2) Filtration of endpoint inputs
End-point is the key feature of big data technology. They help in the processing, storage, and analysis of data. They are very necessary as they ensure that we use only valid end-points during the process.
3) Security of calculations
This refers to securing the various computational tasks that take place during the analysis and implementation of data and logs. It comprises the security of mappers and the sanitization of data.
4) Provenance of data
The origin of data is an important feature as it helps in data classification. It includes graining of access controls and authentication. We also include Validation of data in this feature, and it must be secured.
5) Monitoring data
The security checks and data monitoring should take place in time. However, most people are unable to do it because it generates a large amount of data. It is an important aspect of the security of big data, and we must consider it.
6) Securing communication
The prime method of securing the data is by securing the storage platform, but the application that saves the data storage platform is itself vulnerable, and thus, the security methods must be strongly encrypted.
7) Granular audits
Auditing is also very necessary as it helps in data monitoring. The analysis of data and logs is very necessary as it can help to detect the attacks and spying in future. It is the prime challenge that is faced by the users these days.
8) Security of non-relational data sources
Data stores have many loop holes that create various security challenges. These loop holes decrease the ability of data encryption and logging during the classification process into different groups.
9) Scalability
It is a factor that everyone considers before selecting any tool. The tool must have the ability to learn from the previous mistakes and make itself stronger for the future security challenges which can prove to be a loss of data.
10) Granular access controls
A powerful access control is the most useful aspect of the big data analyzing process. The tool must provide easy access to the big data tool or the Hadoop file system.
