In a world generating approximately 59 zettabytes worth of data per day, it’s only natural for more and more businesses to invest in big data solutions. The valuable insights an organization can gather from such a vast amount of information can quickly become essential assets to achieve different objectives, regardless of the company’s industry or target audience. So, seeing that around 63% of businesses already have some sort of big data solution isn’t surprising.
That naturally should rise in the coming years. Still, the rhythm at which it ends up increasing will largely depend on the companies ability to tackle the diverse challenges that come from big data adoption. There are the development costs, the need for big data engineers to implement the solutions, the technological requirements, and the strategic side to it all. Most companies are aware of these challenges but there’s one that’s equally important that often gets sidelined – data privacy.
The scandals surrounding the misuse of data on behalf of big companies is nothing new. But even when they have propelled new regulatory frameworks to ensure the proper use of data for analytical and commercial purposes, many companies still aim to comply with the basics. This is the wrong approach, as playing loosely with sensitive data from clients can backfire and hurt a company in multiple ways, including financial losses and reputation issues.
It’s high time that companies start treating the privacy issue in their big data efforts more carefully and approach it more strategically to prevent potential problems. What’s the right way to do so? Let’s see some of the privacy threats that come with big data and some clues to build a better strategy to secure that information.
Most Common Big Data Privacy Threats
It’s essential to understand the most common threats to create a truly effective strategy for securing data privacy. By learning what they are up against, companies have a better shot at developing proper measures to combat them. The top 3 enemies of big data privacy include:
- Data breaches: for the most part, the valuable insights from big data originate from fine details, especially from customers’ information, habits, and preferences. All that is extremely personal, so a breach in any big data system can put that data in the wrong hands. These attacks typically occur due to outdated or poorly built systems, weak protection, or targeted malware attacks.
- Data brokerage: there are many companies out there that gather data, build massive databases and sell them to other companies that might benefit from it. However, sometimes the algorithms that do so are flawed, which results in unprotected and incorrect data. The process of selling this problematic data is called “brokerage”, and can quickly compromise the integrity of the buyer’s big data efforts and the privacy of the customer’s data.
- Data individualization: one of the central ideas of securing privacy in big data is developing the ability to extract insights from anonymous data (that is, that it should be impossible for anyone to trace back a specific set of data to a person). However, anonymized data collection is close to a utopia, meaning that there is almost always a way to identify the individual behind specific data, in what constitutes a blatant violation of privacy.
There are other threats for big data privacy that come in all shapes and forms, from human factors (be it involuntary mistakes or malicious actions from the workforce) to poor strategic decisions. Let’s see some of the best practices better to approach the privacy issue from a multifaceted perspective.
Best Data Privacy Practices For Big Data
When it comes to data, the sensitivity surrounding the privacy issue shouldn’t deter companies from implementing big data efforts. There’s plenty of benefits to reap from a proper integration. And while there are inherent risks that can’t be avoided, there are several ways to mitigate them to a point where privacy problems will be highly unlikely.
Some of those practices include the following:
Using Homomorphic Encryption
Big data necessarily means handling data to extract insights. During said process, sensitive data can be quickly revealed, exposing anyone that’s represented by that information. Companies can prevent this by using homomorphic encryption. This encryption method allows big data algorithms to perform the necessary calculations for insight extraction on encrypted data without decrypting it first. The results are also encrypted, making it harder for malicious actors to make sense of any potential information they might be able to catch.
Limiting the Amount of Gathered Data
This feels counterintuitive, as the practice is called “big data” mainly because it works on massive datasets. Yet, it’s important to understand that limiting the data doesn’t mean companies have to keep their datasets small but, rather, keep them relevant. In other words, it’s more about collecting data necessary to inform their business strategy, not collecting data “just because.” Reviewing the collection strategy is a must to define whether the data that’s being collected is needed.
Implementing Real-time Monitoring
Breaches can happen at any moment, so stopping them as quickly as they occur is crucial for damage control. If malicious agents can go unchecked through a company’s systems, they can wreak havoc on them, especially on personal databases. That’s why it’s essential to put real-time monitoring systems in place that control pivotal parts of the digital environment and warn about unusual activities.
Ongoing Staff Training
Human errors are a major cause of breaches and data leaks. That’s why companies working with big data should invest in an ongoing training program to keep their staff updated on the best security practices. This can help inform employees in the best ways to prevent data breaches, like password creation and more secure habits, and training them to spot potential threats (especially social engineering tactics that will be aimed at them).
Preventing Internal Threats
Insider threats go beyond the uninformed staff and reach employees that are actively looking to harm the company for whatever reason. This means that, even when they might know how to prevent attacks, these staffers are contributing to attacks happening. That’s why it’s crucial to implement security measures that tackle the insider threats issue, including separation of duties and least privilege, robust protocols for employee termination, BYOD policies, and risk assessments.
Finally, customers and users can feel as if their trust was violated if the terms in which their data is treated isn’t clear, it’s expressed deceptively, it’s hidden, or can’t be opted out. That’s why companies need to be upfront about their data collection and management, clearly expressing what they are gathering, why they are collecting it, and clarifying the benefit the customers get in return for providing it. Also, businesses should offer opt-out options so users can choose whether they want to share their private information.
The Right Approach Is Custom
All of the above are general suggestions that any company can apply to strengthen their privacy when implementing big data. However, they shouldn’t be understood as the only measures to guarantee privacy, since businesses might have different needs that respond to their specific goals and workflows.
Thus, it’s important to follow a 3-step process. First, realizing the relevance of adopting strong privacy-centered measures for big data efforts. Second, to define a customized approach for the company itself. And third, to execute the plan, monitor it, and adjust it accordingly to keep the privacy levels in top shape. This approach is the right approach – and probably the only way to fight against the privacy issue.