Big Data shares what is commonly termed the V properties or characteristics such as Velocity, Volume and Variety which are amongst the most typical or frequently repeated.
Taking into account security issues and privacy implications with such large datasets is a challenging ordeal that needs a repeatable framework to cover all areas.
Volume is with little to no doubt the most highly targeted characteristic as the expression Big Data fundamentally factors a voluminous amount of information or data that needs to be processed (McCafferty, 2013).
Security and privacy are crucial points in the protection of data and their integrity. It is a challenging enough task to keep these bodies under tight management when the data is of a lesser size, but with Big Data and their datasets having the potential to span multiple datacenters or even physical locations, countries and continents, security and privacy have a much higher risk of being inadequately addressed (Mansuri, 2017).
There are various measures that can be taken to address the innate challenges to Volume as a characteristic in Big Data.
Secure Data Storage and Transactions Logs:
Storing data and/or transaction logs in multiplex storage peripherals provides the added benefit of direct control over each piece of data as it is moved in or out of the platform.
However, as the data grows, doing so becomes more challenging and auto-tiered storage management solutions need to be employed (Buttler, 2017). This does not however keep track of where the data is stored, but merely that it is within the framework itself.
Granular Access Control:
Providing a granular level of access control is absolutely critical to any production system or data storage service that requires an escalated level of security and data privacy (Armerding, 2017).
By adding additional controlled layers of access throughout the platform, one can guarantee that each user or application can only view or edit specific information or meta data that is assigned to them.
On top of our previous point, simply having access controls does not result in a long-term security solution. This leads us to performing granular audits and why it is key to maintaining parity with access controls that have already been set in place (Weathington, 2017).
Audits allow for a planned and scheduled recollection of all users, permissions and access levels. Which data they touch and auditable logs of any internal/external parties that may no longer required the relevant access.
It is possible to utilize a Data Security and Privacy Framework in conjunction with an already deployed or fresh installation to combat intrusion attempts, unauthorized access or loopholes in a Big Data security solution.
While it is impossible to plan for all eventualities, it is highly recommended to plan around existing known issues and learn from past installations from industry experts.
McCafferty, D. (2013) Business Must Address Big Data Knowledge Gaps [Online] BaseLineMag.com, Available from: http://www.baselinemag.com/analytics-big-data/slideshows/business-must-address-big-data-knowledge-gaps.html (Accessed on 2nd March 2018)
Mansuri, S. (2017) How Big Data Solves Cyber Security Issues for Enterprises [Online] DataVersity.net, Available from: http://www.dataversity.net/big-data-solves-cyber-security-issues-enterprises/ (Accessed on 2nd March 2018)
Buttler, P. (2017) 10 Challenges to Big Data Security and Privacy [Online] Dataconomy.com, Available from: http://dataconomy.com/2017/07/10-challenges-big-data-security-privacy/ (Accessed on 2nd March 2018)
Armerding, Y. (2017) The 5 worst big data privacy risks (and how to guard against them) [Online] CSOOnline.com, Available from: https://www.csoonline.com/article/2855641/privacy/the-5-worst-big-data-privacy-risks-and-how-to-guard-against-them.html (Accessed on 2nd March 2018)
Weathington, J (2017) Big data privacy is a bigger issue than you think [Online] TechRepublic.com, Available from: https://www.techrepublic.com/article/big-data-privacy-is-a-bigger-issue-than-you-think/ (Accessed on 2nd March 2018)