Today, no business is strange to the significance of Big data analytics for their progress. It has taken the organizations by storm as an exceeding number of enterprises now depend upon their capabilities to predict demands, and uncover meaningful insights.
The transforming effects of Big data analytics on the digital landscape have been unprecedented. However, enterprises must remember that their growing reliance on leveraging the benefits of Big Data may cost them dearly on one front, that is, privacy.
The valuable insights attained through Big data Analytics has the effect of obscuring its unpleasant side from the vision of many businesses. This unpleasant or ‘dark side’ refers to the big data privacy risks, vulnerabilities, and threats associated with Big Data.
It thus becomes essential to open our eyes and confront the major security issues being created by Big data analytics and how they can be tackled.
Big data analytics often prompts organizations to initiate actions that, more often than not, result in involved parties’ privacy breaches. Customer details comprise a major part of this analytics.
These details tend to be confidential in nature. Many businesses like restaurant chains, online marketplaces have analyzed Big Data at the cost of exposing extremely sensitive information about their employees and customers.
Privacy breach poses one of the most pressing big data privacy risks that need to be addressed. It becomes essential to study the nature of security-centric organizations. Virtual private network providers like NordVPN have established powerful norms for customer data protection. These focus on maintaining anonymity and security foremost. Such customer privacy measures can mitigate such big data privacy risks to a great extent.
The purpose of Big data Analytics is to provide insights. These insights are gleaned from innumerable data sets. It thus becomes difficult for customers to maintain the privacy of any facet of their identity. The sheer impossibility of attaining anonymity is one of the biggest big data privacy risks. According to Rebecca Harold, CEO of The Privacy Professor, even de-identified data does not eliminate privacy threats. If this issue remains untreated, it will soon become entirely impossible to anonymize data through parties that cannot be re-identified.
To combat this threat, it is highly advised to use several anonymization techniques like randomization and generalization, among others, instead of only one of them. It will prevent the data from re-identification.
Thanks to big data analytics, businesses can now easily find out race and ethnicity-related information about people and unleash rampant discrimination. Through predictive analysis, companies get to know the race and gender of a person, which they use as a criterion to decide whether to offer them their services or not.
One of the major big data privacy risks relates to this discrimination becoming ‘automated’ and thus more difficult to detect. As a solution to this threat, a Big data analytics company should work to innovate Big Data algorithms and make them free of bias.
Take a cue from organizations like AlgorithmWatch and The Algorithm Justice League that spread awareness and provide education for effective identification and removal of biases in existing algorithms.
In a big data environment, it is incredibly challenging to verify the uniqueness of a patent. You cannot possibly find out whether it infringes copyright as there are loads of data available against which their authenticity requires to be checked. Many data repositories are obscured with walls that deny access.
It is critical to gain a better understanding of the extent of data that can be copyright protected. The majority of data processed in the context of Big Data Analytics cannot be considered original. Hence it cannot avail copyright protection. However, individual data can become unique if connected with original information or displayed uniquely.
This is one of those big data privacy risks that can be hard to believe. After hearing about the enormous benefits of big data analytics, it can come as a surprise when you find out that the analysis can be false!
The source of its inaccuracy lays primarily in its incorrect algorithms and error-laden data models. It renders the big data diagnosis wrong and in the long run, impacts the lives of people in a negative manner. Complex data analysis models, when employed without employing stringent validation measures will set the ground for the generation of inaccurate insights and decisions. It will thus make way for an enterprise’s downfall.
To deal with it, start by defining precisely the data you need according to your business goal. The data should be cleaned and organized properly to make it better suited to be segmented for analysis.
Although these big data privacy risks are huge, nevertheless steps can be taken to minimize or limit them. One of the principal ways to eliminate such threats is to utilize big data analytics to expose issues for the betterment of society. Being mindful of these risks during the initial stages of your big data analytics project and establishing a set of policies for analytics will help you to effectively mitigate these risks.
There are ways to resolve overfitting, like cross-validation and the addition of a regularization term. Nevertheless, they do not guarantee a permanent solution to this problem.