What Are the Biggest Privacy Issues Associated with Big Data?

As the influence of the big data phenomenon grows increasingly widespread, with an increasing number of organizations relying on big data analytics for the fulfillment of their daily operations- big data has revolutionized the digital landscape.

When it comes to running a digital enterprise, or an e-business amidst the complex threat landscape of today, the valuable insights generated by big data play a crucial part in aiding businesses to flourish. Some aspects of running a business in which big data insights play a crucial role include predicting the most popular items for the season, along with providing businesses the basic framework to improve their brand-consumer relationship from.

As more and more companies amalgamate the insights generated by big data into the backbone of their business, and as a growing number of organizations harness the power of big data to make their enterprise stand out from the competition, most enterprise owners and security experts tend to willfully ignore the ‘dark’ side of big data.

As ominous as the phrase ‘the dark side’ sounds, perhaps even darker is the multitude of threats and vulnerabilities associated with big data that often get pushed under the rug. With the power harnessed by these big-data insights growing at a rapid pace, the privacy issues associated with big data also catapult to the top of the ‘security and privacy concerns’ totem pole.

While the IT landscape brinks on the edge of yet another breach or vulnerability, it is highly important that enterprise owners grasp the reality of the big data situation, and instead of viewing big data as this perfect phenomenon, and come to terms with the multiple privacy loopholes present.

In an attempt to aid our readers in realizing the significance of the multiple security issues with big data generated insights, we’ve compiled an article that highlights some of the most prominent privacy concerns, along with some ways through which those vulnerabilities can be combated.

#1- Obstruction of Privacy Through Breaches

In the modern digital landscape of today, where phenomenons such as the “filter bubble,” and “personalized marketing” are on the rise, many individuals fear that they live with their privacy, particularly their online privacy in a state of constant decline.

Although that might be true to a certain extent, if the fragile state of cybersecurity continues on the path that it is today, our future might just ring true of an Orwellian nightmare. And if that wasn’t enough, the magnitude of the situation gets amplified further when we account for the privacy breaches that are made possible through big data insights and analyses. We should take cues from security-centric organizations, which are typically ahead of the curve. Virtual private network provider NordVPN has received mostly positive reviews in Canada thanks to its customer data protection policies.

Typically, a huge portion of big data insights consists of predictions being made regarding the customer’s details. Oftentimes, these details tend to be extremely personal in nature, which is why even the mere chance of them falling into the wrong hands, is enough to eradicate any trust that individuals have in the organization.

Realizing the significance of privacy, and the value that sensitive information bears, it is critical to an organization’s survival, that they implement measures that prevent the obstruction of consumer privacy.

#2- It Becomes Near-Possible to Achieve Anonymity

Despite receiving severe scrutiny, complete anonymity on the internet is still considered to be a superpower of sorts, often employed by undercover journalists and citizens living in geo-restricted zones.

As organizations employ big data analytics, even the mere notion of having anonymized data files becomes impossible. Since big data insights are based on a wide variety of raw data sets, there is a high possibility that consumers could have their identification factors exposed, which eradicates any semblance of privacy that the individual might have.

Moreover, in the rare instance that a data file is made to be completely ‘anonymized,’ several security teams combine these valued files with others, to make the process of identifying an individual quite easy. Anonymization is further complicated by the fact that nearly every SME that does business online relies on finance, invoicing, and accounting software hosted by third parties in the cloud, many of whom have varying data privacy practices.

In addition to the already meager privacy that individuals are given, accounting for the interconnectedness amongst devices, much thanks to the ever-expanding IoT that houses millions of smart gadgets, the notion of obtaining privacy becomes even more obscure.

#3 – Data Masking Met With Failure in a Big Data-Driven Setting

In an attempt to protect their confidential information from hackers and cybercriminals, most organizations utilize the procedure of ‘Data masking.’ As its name suggests, data masking, also referred to as data obfuscation, is the process through which real data is hidden by other less important characters or data sets. Typically, data masking is deployed to veil sensitive information from unauthorized individuals.

In most enterprises, the primary function served through data masking is the protection of confidential data from ending up in the wrong hands, which does not necessarily ring true for big-data driven settings. This is particularly true for software enterprises, which rely on software-as-a-service (SaaS) marketing agencies to grow. SaaS data aggregation is particularly susceptible to data breaches, as most of the customer data now lives in the cloud.

If not utilized properly, data masking could result in complete failure, compromising the security, and subsequently, the privacy of multiple individuals through big data analytics. Since the amalgamation of big data generated insights is a trend that has only recently started to gain popularity, oftentimes, organizations tend to ignore the risks associated with big data, further amplifying the dangers to their consumers’ privacy.

The only solution to the conundrum posed by the functioning of big data alongside data masking is for companies to establish a stringent policy that lays out the rules for data masking, along with ensuring that those rules are followed by each employee.

#4 – Big Data Analysis Isn’t Completely Accurate

Perhaps the surprising issue seen with big data, is that contrary to popular belief, the analysis generated by big data isn’t as accurate as we previously thought it to be. Although the insights formulated by big data are powerful, they can also be critically flawed at times, further contributing to the privacy issues we’ve mentioned so far.

Typically, an inaccurate big data analysis is rooted primarily in flawed algorithms, incorrect data models, along with misplaced data about individuals. Not only does running of a poorly-done big data diagnosis contribute to the lack of validation for data, but it is also a source of direct harm for consumers since it results in the loss of jobs, false misdiagnosis, and the denial of essential services.

Furthermore, if an organization were to run with faulty big data analysis, their blind trust could potentially send them tumbling down a rabbit hole of failures, and even cause them to close down.

#5 – Copyrights and Patents Are Rendered Irrelevant

Another issue that renders the inclusion of big data within organizations pretty painstaking, is the fact that in a big data-driven setting, obtaining patents becomes extremely challenging.

One of the primary reasons behind the difficulty in obtaining patents in a big data environment is that it takes an excruciatingly long amount of time to verify the uniqueness of the patent, amidst a monumental database of information available.

Furthermore, in a big data environment, copyrights are rendered to be irrelevant, since big data makes the manipulation of data highly possible, which also sends the royalties associated with the invention of something original, tumbling into dust.

#6 – Discrimination Issues

As humanity takes on a new turn and welcomes with open arms the advent of a digital age, one might assume that we’d leave racism and blatant discrimination in the past, but unfortunately, they are still real issues that continue to wreak damage.

Although discrimination exists in almost all sectors and industries, with the inclusion of big data insights and analytics, companies can now find out the race of an individual and leverage their piece of information against them.

For instance, if a person were to apply for a bank loan, through predictive analytics, the company could find out the person’s race, and reject them on that basis- which is now a named phenomenon known as “automated discrimination.”


At the end of the article, we can only hope that we’ve made clear to our readers the reality of the “Big Data Situation,” along with the plethora of privacy issues that arise in a big data-driven setting. Having said that, we can only hope that the privacy concerns we’ve mentioned are prioritized by organizations, who hopefully take a more active approach in resolving these concerns.



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