Data anonymization: what, why and how of anonymization (2023)

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In the age of big data, privacy is an issue of growing concern for consumers and businesses alike. Especially as more services we rely on become available online and personalization of marketing and product recommendations increases, companies are always looking for smarter ways to use data.

However, these innovations must not jeopardize secure storage and data protection. When companies use customer data, they have a responsibility to protect it, and data anonymity is a critical part of a good privacy and security strategy.

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Some reputable companies have been hit with severe sanctions for violating European Union regulations.General Data Protection Regulation (GDPR). GDPR enforcement shows no signs of slowing down. In the United States, regulations such asCalifornia Privacy Rights Act (CCPA)order the establishment of a national data protection authority to deal with suspected breaches and compliance.

No company wants to be an accidental or negligent infringer. Additionally, ensuring privacy compliance is an increasingly important way to build consumer trust in your brand. Just as a data breach can be a surefire way to lose a hard-earned reputation.

We look at how user data is anonymized, where that anonymity is needed, and how companies can take steps to comply with privacy regulations.

Read on to learn more about:

• anonymization 101
• How is data anonymized?
• Anonymization best practices
• Effects of data anonymization

To better understand data anonymization and its importance to compliance, it's helpful to review some key terms. It's a complicated subject with many different laws and regulations, so it's important to understand the basics first.

We start with what we mean by anonymous data. De-identification refers to the removal ofPersonally Identifiable Information (PII)records to protect privacy. In other words, data processors must be able to process the information, e.g. B. for analysis and research, where there is no recognizable connection or the person from whom they originate can be directly identified.

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Pseudonymization is a form of this work in which personal identities are replaced by artificial identifiers or pseudonyms. For example, removing a real name and replacing it with "Jane Doe" is pseudonymization. Although in real life it is usually a random identification. The key to recognition is that anonymous data can be reassigned to the person it came from, so the information needed to do this must be kept separate and secure to prevent data breaches.

In contrast, anonymization is a stricter anonymization standard. Refers to the process of permanently deleting PII so that the identifying link can never be re-established. In other words, anonymization and pseudonymization separate the individual from the data but keep the linking information stored separately, whereas anonymization requires that there is no risk of re-identification between the individual and the data.

We'll get into technical details in a moment, but first let's look at what the law says about anonymity. In the United States, in 2010, theDodd-Frank Wall Street Reform and Consumer Protection Act, (we'll refer to it as the CPA for simplicity), which provides an important overview of consumer rights to their data.

Among other things, CPA gives people access to their own financial data and the ability to move or share it with others. This has been critical to trends such as the push towards open banking and allowing new businesses to compete with traditional institutions. These financial changes can affect e-commerce as well.

However, data protection is still a work in progress. The United StatesConsumer Financial Protection Office (CFPB)and international bodies regularly review existing data protection laws, and new laws continue to be drafted in countries around the world in light of new technological developments and the new risks these advances bring.

In 2018, the GDPR came into force, forcing companies that process EU citizen data to comply with far-reaching regulations. Since then, the GDPR has also influenced other regulations such asCCPA.

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UnderGDPRand similar regulations that use an "opt-in" model, data controllers must inform users when data is being collected and grant an individual the right to prevent such collection and processing at any time. Individuals are also given express ownership of their data in the sense that they must be able to port personal data from one system to another.

An opt-out model, the most commonly used model in the US to date, requires obtaining consumer consent only before personal information is collected (or, in some cases, shared), not before it is collected.

This is where the above distinction between anonymization and anonymization comes into play, as data that has been fully anonymized is not subject to these consent requirements, but data that has only been anonymized is. Perhaps not surprisingly, this poses some challenges for companies that store personal identifiers and rely on users' fingerprints to provide their services. For example, anything that monitors users' online behavior.

Most companies today collect some form of personal data, especially in e-commerce. For example, to provide users with a seamless payment experience, businesses need billing software that includes features like payment reminders and automatic billing for returning customers. But to do that, a business must use browser cookies and store personal information and payment information.

So how can data be anonymized correctly? Or maybe a better question would be: can the data really be anonymized? This is a big issue for privacy professionals who are always looking for ways to make data storage more secure.

There are several ways in which personally identifiable information, such as names, special security numbers, physical or email addresses, etc., can be separated from its individual owners:

• masking.Some common data masking techniques include word or character substitution and character rearrangement. But, as you can probably guess, this information can be re-identified, so it's not true anonymity.
• Generalization.This technique removes sensitive parts of the data without changing important information. For example, remove some parts of home addresses and keep the general geographic location.
• Swap/shuffle/swap.As the name suggests, this method rearranges the data so that the same data points are in the dataset, but not in the original order.
• Disturbance.This technique uses a proportional factor to add what data scientists call "random noise" to a dataset. This can be a complex process, but random noise can also be filtered out, so this method isn't foolproof either.
• synthetic data.This is the only technique that may be acceptable under GDPR and similar regulations. it impliescreate artificial datasetsthat resemble the original dataset (that is, retain the relevant properties). While the GDPR does not explicitly speak of synthetic data, it does say that the regulations only apply to data that has a connection to "an identifiable natural person", which is not the case with synthetic data, even if it mimics real user information. . .

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Not all user data is considered equal

All data protection laws, from GDPR to CPRA, have different definitions of personal data. Sensitive data has even more requirements. We can help you understand.

Perhaps the most common application of data anonymization has always been in healthcare, where providers need to store medical records in a way that does not endanger individuals in the event of a breach. However, big data has paved the way for most companies to start thinking about privacy compliance, be it for e-commerce stores, social media marketing, etc.

For online businesses, privacy needs to be at the heart of digital processes, including websites. FORConsent Management PlatformIt can be an important tool for securing user consent and achieving privacy compliance.

There are some obvious privacy benefits for online users. It's not hard to see how widely shared information such as health records, account details or contact information can be dangerous. There were data breaches due to anonymization failures, which highlighted the risk of breaches and the need to enforce privacy regulations.

As more consumers and Internet users express concerns about privacy, they are also showing preferences for things like personalization in recommendations and advertising. This poses a challenge for marketers because while high-level performance metrics such as ROI and various on-page SEO KPIs can also be tracked with de-identification, de-identified data cannot be used for marketing efforts. direct marketing or personalization.

However, privacy can also be used to marketers' benefit when companies make privacy part of their brand. In fact, building trust in the company is considered one of the best ways to build brand equity to drive revenue growth, and letting customers know that their information is protected is a crucial part of building that trust. . By following these tips and investing in the right compliance monitoring tools, organizations can better ensure customer data security.

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We do not provide legal advice and strongly recommend that you consult with a data protection and privacy attorney to ensure that your business is in full compliance with US data protection laws. If you're interested in learning more about how a consent management platform can improve your data strategy, speak to one of our experts today.