Privacy has been and will continue to be an issue for many people especially when it comes to social networks and big data analytics. An article published on Secure World listed these 10 privacy problems when it comes to big data analytics:
1. Privacy breaches and embarrassments.
2. Anonymization could become impossible.
3. Data masking could be defeated to reveal personal information.
4. Unethical actions based on interpretations.
5. Big data analytics are not 100% accurate.
7. Few (if any) legal protections exist for the involved individuals.
8. Big data will probably exist forever.
9. Concerns for e-discovery.
10. Making patents and copyrights irrelevant.
Now, just by looking at the list above, it is very clear that big data analytics, apart from being very useful in various part of our lives as individuals and for us whole as a society, can bring along many privacy issues that are unethical, discriminative, and that make all of us concerned about what data is out there that some day might destroy everything we’ve got.
“The data files used for big data analysis can often contain inaccurate data about individuals, use data models that are incorrect as they relate to particular individuals, or simply be flawed algorithms (the results of big data analytics are only as good, or bad, as the computations used to get those results). These risks increase as more data is added to data sets, and as more complex data analysis models are used without including rigorous validation within the analysis process. As a result, organizations could make bad decisions and take inappropriate and damaging actions,” according to author Rebecca Herold.
Nevertheless we have to thank big data which is playing a key role into making machine learning no longer futuristic. Various companies like IBM, HP and Microsoft can now use machine learning and in a way ‘reinvent’ themselves after cloud’s big wave of publicity began to fade away and was no longer useful to the same extent is was some time ago.
Big data is helping many companies to improve the way they function and help their businesses do better in the long term. But in order for companies or organisations to stay away of big data’s uncorrect analysis and protect their image, according to Herold they are advised to do the following:
1) consider at least these ten privacy risks during the planning stages of your big data analytics strategies,
2) establish responsibility, accountability, policies and procedures for big data analytics and use, and
3) incorporate privacy and security controls into the related processes before actually putting them into business use.
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