10 Tips for Securing Big Data

Why a breach of big data can be a concern

Big Data is a valuable asset for an organization used to discover hidden patterns and trends of data for business growth and analysis. It is a newborn technology, comes into existence due to the gigantic increase in the size of data in recent years.

In the age of technology, it is a powerful tool to take competitive advantages over other companies. It is a major source of analyzing real-time streams of big data through advanced technologies like Hadoop, NoSQL, etc.

Breach of data security is a major concern of big data because it contains a large amount of information related to employees, products, customers, patients, partners, weathers, trends, aptitude, longitude, social media, etc.

Any breach of personal as well as organizational information may cause serious security concerns as well as a privacy violation. So, the security of data needs to be strictly focused to save from misuse or breach of security.

10 Tips for Securing Big Data

Security of collected information is very important for all stakeholders, so the following are some important tips to secure the big data from security breaches (Divya, Bhargavi, and Jyothi 2018).

1.     Secure Your Computation Code

Malicious data can be avoided by implementing code signing, access control, dynamic analysis of computational code. Precautionary measures can be taken to avoid any suspicious code that can violate the big data processing trust. Hadoop is considered the most powerful framework for processing Big data on commodity hardware. It can help secure and prevent attacks through a special map and reduce framework.

2.     Implement Comprehensive Input Validation and Filtering.

There should be proper validation and filtration system on all internal and external sources of data. It can be an efficient algorithm that evaluates the key input validation and filtering according to data being loaded and processed.

3.     Implement Granular Access Control.

A special authorization system should be implemented to access Big Data. Accessibility to sensitive data is a more critical issue, so limited and trusted personnel are allowed to use these rights in a secure environment. All ad-hoc queries should be verified before data computation and processing. By default, free access to sensitive data should not be allowed without a proper procedure to access important data resources.

4.     Secure your data storage and computation.

The data storage and computation are most sensitive phases of big data analytics where data leakage can take place. Data encryption for sensitive data and special audit administrative access on Data Nodes(DNs) are major steps in this situation. Application Programming Interface(APIs) also has the main role in data storage and computation.

5.     Establishing Granular Audits

It is kind of checking and monitoring the activities critically to avoid any type of big slack in security. It checks all happening with big data infrastructure but works independently separate from regular Big data. A different could or network can be set up to host the infrastructure of an audit system(Lamb 2016).

6.     Review and Implement Privacy-Preserving Data Mining and Analytics.

Algorithm design plays an important in mining and classifying data for the preservation of sensitive information. It might be helpful to reduce the disclosure of sensitive information. A proper policy regarding big data management should be identified to avoid any inconveniences during data processing.

7.     Real-Time Compliance and Security Monitoring

More updated and real-time data provide a high level of accuracy in predictions and decisions so it is best to grab it head-on with real-time security and analytics at each level of the stack. According to Cloud Security Alliance (CSA), organizations should apply big data analytics using tools like secure shell (SSH), Kerberos, internet protocol security-IPsec to get a handle on real-time data.

8.     Centralize accountability. 

Normally, big data reside in diverse organizational data sets around the globe. It is better to centralize the accountability to ensure the consistent security policy implementation and privileges across the organizations(Thor Olavsrud 2012).

9.     Separate your keys and your encrypted data

Encryption of data is a very useful technique to secure the important data but it should not be a hanging key near to locks. It means the encryption key should be stored on a separate server through a key management system that ensures the safety and security of protected data(Thor Olavsrud 2012).

10. Hiring a Professional Team for Big Data processing

Sometimes, untrained people cause a big security risk, so at the time of contract, the organization should clearly show its policies regarding data security and all concerns. Technology experts must monitor their team activities and interactions with data. Unauthorized persons should not be allowed to access the data.

 

 

References

Divya, K. Sree, P. Bhargavi, and S. Jyothi. 2018. “International Journal of Computer Sciences and Engineering Open Access Machine Learning Algorithms in Big Data Analytics.” (October):157–66.

Lamb, Eleanor. 2016. “Top 10 Ways to Secure Big Data – MeriTalk.” Retrieved May 17, 2020.

Thor Olavsrud. 2012. “How to Secure Big Data in Hadoop | CIO.” Retrieved May 17, 2020.