Big Data Privacy Risks and How to Avoid Them
In this article, we explore how big data privacy policies have changed over time and how companies can adjust their strategies to stay compliant.
Data is the new gold. Information is gathered from a variety of sources: IoT devices, M2M communication, facial recognition software, etc. These extensive datasets provide companies with immense opportunities to better understand customer needs, detect important correlations between seemingly non-related variables, support decision making, predict customers’ behavior, weather conditions, market fluctuations, you name it.
Collecting, structuring, and processing data is not the easiest task, but it’s perfectly attainable even for small businesses. Given that companies can extract so much value at a relatively low cost, this data-collection gold rush shouldn’t be surprising.
However, with great power comes great responsibility. Any piece of personal information that becomes accessible to a company is a serious opportunity to invade privacy and damage an individual’s fundamental rights. Unauthorized collection, weak protection, or irresponsible processing imply serious risks for both an individual and the company. Despite the immense benefits that big data analytics brings to the table, data should never jeopardize privacy. Balancing the risks and power of big data should be one of organizations’ top priorities.
Today, big data consulting is as relevant as never before: failing to comply with security and privacy regulations can result in huge fines, lawsuits, and permanently damaged public image. For example, GDPR infringement fines can go up to 20 million EUR or 4% of a company’s annual revenue. Moreover, authorities can prohibit companies to process data at all, which means going out of business in many cases.
Let’s examine the big data privacy issues companies might encounter and the measures they need to take to ensure their software product development within the legal frame.
Big Data Privacy Challenges
Data analysis is not a revolutionary concept. Businesses have been using data analytics for decades to gain competitive advantage and increase profits. The consequential question here is, why did big data privacy only become a pressing issue recently?
The problem is in the scale. The volume and variety of data have skyrocketed due to significant technological advancements, making privacy concerns more apparent. In this regard, we can identify these data privacy challenges:
Lack of Control
The extensive variety of data sources across different systems makes control over personal information much more complex. In many cases, people have no clue what personal information is gathered and how it is processed. This raises a significant technological challenge for companies to inform their users accordingly. Data captured with security cameras or web search cookies are great examples of often ungoverned data processing.
Companies collect data for a reason. For example, healthcare organizations need your medical history to treat you safely. However, when this data is placed in the hands of third parties without your consent, it can raise many ethical concerns.
The proliferation of AI algorithms allows us to combine and process seemingly unrelated data sets and deanonymize highly sensitive personal information. This implies a significant threat to one’s confidential information. One of the most famous scandals related to data inference happened in 2012 when Target identified a teenage girl’s pregnancy by analyzing her web search history and sent her discount coupons for associated products.
Excessive Automation and Profiling
Companies using big data in ecommerce, for example, are looking to make profit thanks to it. Targeting plays a huge role here. The more you know about your customer, the more effective your marketing and pricing strategies.
However, customer profiling can often lead to discrimination and unfair allocation of benefits based on race, age, location, etc. For example, today’s online retail platforms frequently apply ML-based pricing differentiation, which proved to be an effective method for driving revenue. However, in case of incomplete data, such practices may lead to mistakes that deprive perfectly qualified people of their entitled prices or bonuses.
EU Data Protection Acts
With the all-permeating digitalization of our world and the rapid advancements of big data, the EU had no other choice but to enforce new regulations to protect personally identifiable information of its residents. The GDPR (General Data Protection Regulation) has substituted the Data Protection Directive, which was first introduced in 1995. The new regulation’s main goal is to make companies more responsible for how they collect, store, and process data of EU citizens. The GDPR came into force on May 25, 2018, and applies to any country within the EU, regardless of the legislative stance of local governments.
There are two main roles identified in the GDPR:
- Data Controller is the main body, which outlines the purposes and ways of processing personal data. Essentially, this is the business, which needs data to operate.
- Data Processor, in the majority of cases, is the company responsible for creating software that processes data for one’s business needs. This is of highest importance, as now the responsibility is split between both roles, making software development companies pay more attention to securing data privacy from the get-go.
The GDPR defines personal information as every little piece of information that can be linked to a particular person. Besides obvious characteristics like full name, occupation, race, and physical characteristics, any hints left on the internet that can be traced back to a person are subject to GDPR compliance. For example, the nickname you once used on a long-forgotten fan page ten years ago can still be used to identify you as a person and, therefore, is also considered protected under the GDPR.
Next, let’s briefly go through the main principles set by the GDPR:
- The reason for collecting, storing, and processing data must be supported by a legal document, such as a written user consent or a specific contract.
- The above mentioned contracts must be understandable, transparent, and concise. In a nutshell, vaguely identifiable terms or a tiny blueprint are not tolerated by the GDPR.
- Data may be collected for explicit reasons for a particular business. Basically, businesses can gather only data identified in the consent.
- Businesses may collect only the minimum necessary data for their operations. It’s important because today’s automation tools and seemingly infinite cloud storages make it tempting to collect data just for the sake of it or even for ill-intended purposes.
- Data must be accurate and up-to-date. Any small misinformation may constitute GDPR infringement. Companies need to be equipped with the latest data cleaning techniques to ensure the compliance. Inaccurate data has to be immediately erased; delays are not tolerated.
- Individuals may be identified for no longer than necessary. For example, if a service is no longer provided to a specific person, this person’s data may not be stored or processed.
- Companies now take full responsibility for any data loss, damage, or unlawful processing. Anonymization systems are required to fully protect users’ identity, keeping their data confidential.
- Businesses must keep records of all data-related activities. Any act that concerns data collection or processing must be documented and justified. Authorities now have the right to request documents that prove GDPR compliance.
These rules concern both old and new software products. It’s also worth noting that even if the company doesn’t currently have EU clientele, it still may be the case in the foreseeable future, so the GDPR compliance is required. Unsurprisingly, it is much easier to develop new software with GDPR compliance in mind than try to tailor existing apps to the new regulations.
Consider following these principles when designing software products:
- Your software must include tools that can immediately delete all information about a particular user. This concerns the ‘right to be forgotten’, which means that users can request the deletion of all of their personal information whenever they want. Your software must include tools that allow you to seamlessly transfer all the required information from one service provider to another. For example, a user should be able to transfer his or her healthcare data to another facility upon request.
- In case of a security breach, users have to be notified within 72 hours. Although companies now put an extra emphasis on cybersecurity, data breaches are still common. It is highly advisable to design a built-in automation tool that notifies both your tech team and users in case of a data breach.
- Organizations must ensure the highest level of privacy by default. For example, if an app doesn’t require name and surname to operate, an automatically generated nickname should be offered during registration.
Companies can now collect and process data only after users have been informed and given their consent. The current avalanche of disclaimer pop-ups and boxes to be ticked on every website might be irritating, but this is the only way to ensure that the GDPR standards are met.
Tips on Staying GDPR-compliant
As fines can go up to €20 million, being compliant in regard to big data privacy is now of utmost importance. Let’s see which steps companies can take to avoid regulatory bumps on the way.
Encourage awareness from top to bottom. Ensure that everyone involved in your software development is aware of the GDPR by providing sufficient training. The more your employees know about data privacy compliance, the less time and resources will be needed to assure it. However, it’s crucial to limit the burden of responsibilities for your developers and managers. At the end of the day, their job is to make flawless software according to your specifications.
Any stress caused by the requirement to meet regulations can decrease productivity. This is why you will most likely need a data protection officer (DPO) whose sole responsibility will be to make sure that the end product meets regulatory standards. The DPO will also be in charge of maintaining documentation and ensuring that released products or services collect and process data legitimately.
Make documentation mandatory. This is where many companies often get confused. Your service may be fully compliant with the rules, but it still needs a documented proof of it. This concerns both software projects and business operations on the whole.
Assess compliance upfront. In addition to the established ways of delivering products and services, a privacy strategy should also be among the early phases of software development. An effective method of ensuring that your service can be delivered within legal boundaries is to go through the Privacy Impact Assessment (PIA). It not only helps with early detection of privacy risks but can also serve as solid documentation in case of privacy disputes.
Be reasonable. The whole GDPR case is intended to make companies reconsider the reasons for data collection. Given that data is arguably the most valuable resource in today’s business world, companies were following ironclad logic behind their lavish amounts of stored information: ‘If we can gather a hundred times more data at the same cost, what’s the reason not to do it?’ However, as the GDPR came into play, the rules of the game have changed. From now on, less data correlates with less risk. Determine which datasets are really necessary and anonymize or encrypt it as much as possible.
Invest in privacy. Earlier this year, Cisco surveyed 2,800 security professionals from wide a wide range of industries in 13 countries to determine the ROI of privacy. The majority of the respondents reported they saw very significant benefits of their privacy investments:
As you can see from the graph above, the motivation for investments goes far beyond avoiding fines and penalties. In simple terms, for every invested dollar, each company received an average of $2.70 worth of benefits. In terms of privacy spending, the number of lines of code and the number of users directly correlates with the amount of money and effort you need in order to ensure sufficient privacy.
Implementing data privacy policies is not solely about risk management. It’s a matter of business ethics and a secured digital future for everybody. Privacy by design should be an essential part of any big data initiative. This implies that security and privacy shouldn’t be an afterthought of software development, but a deeply rooted feature and a core requirement.
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