Introduction

Big data organizations are using big data to create change in different industries. The Internet industry, E-commerce industry, Telecommunication industry, and Medical industry make up the common big data organizations today. Beyond the extraordinary promises of big data, there are a lot of security and privacy challenges these big data organizations face. Big Data User Privacy Protection, The credibility of big data and Mobile data security issues/Malware attacks(Gupta, 2018) are three main threats to big data security organizations.

Three main threats to big data security for organizations and mitigations:

1. Big Data User Privacy:

Big data organizations have the responsibility to properly deal with big data user privacy through the life-cycle of big data, from the collection, big data processing, data storage, and big data application. Among the big data privacy worries are location privacy protection, identifier anonymity protection, and connection anonymity protection. Removing identifying privacy data such as name, and location data does not still guarantee the privacy of big data users. With the powerful algorithms present today, it is possible for patterns to be defined from big data that will lead to user privacy being compromised. Big data user privacy for users if not effectively managed there is no guarantee to user privacy.

Mitigations:

1. Beyond user identifiers, proper supervision, and corporate self-regulation are all required to ensure user big data privacy protections.
2. Big data organizations have to ensure secure and reliable data storage, comprehensive data backup and management.
3. Data privacy has to be maintained throughout the life cycle of big data. Encryption techniques can be utilized for protecting data(Sriram, 2022).

2. The credibility of big data:

The variety of big data makes it challenging to maintain big data credibility. Among the common threats to the credibility of big data include forged or deliberately made data, and distortion of data in its transmission(Gupta, 2018). Given that big data comes from different sources, it makes it hard to discern how credible the data can be. Imagine internet companies that have to make decisions based on user-generated content from sources such as review forms which in most cases comprise both fake reviews and real reviews. Another common scenario is the distortion of data in transmission which end up not representing the rightful data. These distortions in big data are a common challenge faced by data scientists today.

Mitigations:

1. Gupta, (2018, p.233) states that “users of big data should be able to understand the reliability of data based on the authenticity of the data sources, data transmission channels, data processing, to prevent the analysis from obtaining erroneous results”.
2. Data encryption tools should be used by big data organizations to preserve data authenticity.

3. Mobile data security issues/Malware attacks:

The Internet of things(IoT) is postulated as the biggest consumer of big data. The application of big data in the world of mobile devices has drastically improved the functionally and application of these devices. Such intelligence devices are also very susceptible to malware attacks resulting from their big data applications. Malware attacks on these devices can quickly lead to big data privacy problems where users' identity information and confidentially can be stolen by hackers. Keeping track of such sophisticated attacks is a huge security and privacy challenges to big data organizations.

Mitigations:

1. A common mitigation process can be strong data encryption algorithms to prevent privacy loss.
2. Big data organizations should use cloud platforms to provide such great infrastructures to prevent direct access to mobile data.

Conclusion:

Big Data User Privacy Protection, The credibility of big data and Mobile data security issues/Malware attacks are the three main threats to big data organizations. To stay ahead of these threats, organizations have to utilize the tools necessary to ensure that big data privacy and confidentiality are preserved, organizations have to ensure that supervision and encryption tools are readily available through out the life cycle of big data to preserve and prevent both the credibility of big data and mobile data security issues/malware attacks.

Reference: Gupta, N. K. (2018). Addressing big data security issues and challenges. International Journal of Computer Engineering & Technology, 9(4), 229-237. https://iaeme.com/MasterAdmin/Journal_uploads/IJCET/VOLUME_9_ISSUE_4/IJCET_09_04_025.pdf Sriram, G. K. (2022). Security challenges of big data computing. International Research Journal of Modernization in Engineering Technology and Science, 4(1), 1164-1171. https://www.irjmets.com/uploadedfiles/paper/issue_1_january_2022/18527/final/fin_irjmets1643004117.pdf