Despite diverse protection measures applied by organizations, data breaches involving Personally Identifiable Information (PII) still cause substantial financial losses across various industries. Between March 2022 and March 2023, compromised customer and employee PII cost organizations $183 and $181 per record, respectively, according to the 2023 Cost of a Data Breach Report by IBM Security.

Anonymization is one of the most effective data protection measures that can prevent personal data breaches or, at least, decrease the cost of each breached personal data record. In this article, we look at what data anonymization is, examine its types and major challenges, and provide you with the best practices for anonymizing data in your organization.

What is data anonymization?

Data anonymization is a process of transforming sensitive personal information into anonymous data that cannot be linked to a specific person. This process involves removing or editing PII. Depending on uniqueness and the ease with which an individual can be identified, PII can be divided into two groups:

Data anonymization helps companies protect the privacy of their clients’, employees’, or partners’ sensitive information while still allowing them to use it for business purposes. Thus, if malicious actors manage to compromise data that was previously anonymized, they won’t be able to easily identify who that data belongs to. In turn, data anonymization helps prevent identity theft, financial fraud, stalking and harassment, discrimination, and other privacy violations.

According to the 2023 Data Breach Investigations Report by Verizon, personal data is the most commonly breached type of data in the following industries:

Various industries are still losing a lot of personal data as a result of data breaches. The statistics above emphasize the importance of implementing tailored measures for personal data protection.

Personal data breaches not only signify gaps in an organization’s security, but can also result in loss of customer trust and revenue, non-compliance fines, and legal liabilities. 

By hiding or deleting PII from collected data, organizations can minimize the damage arising from unauthorized access to internal data assets. This is what anonymization is aimed at.

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Types of data anonymization

Anonymization of data can be performed in a variety of ways. Let’s look at the most common data anonymization techniques.

Data masking involves the alteration of data by shuffling characters, substituting words or characters, or encrypting them. This technique helps to generate a fake but realistic version of a data asset. 

Synthetic data generation is used to craft artificial datasets based on a real dataset while preserving the statistical properties of the original data. This method enables comprehensive testing, analysis, and data sharing without compromising PII.

Data generalization reduces the identifiability of sensitive information by replacing specific data with broader, more general details. It allows for removing certain identifiers while retaining data accuracy.

Data swapping involves replacing real data elements with fictitious but similar values. This type of anonymization breaks any direct link between the data and the individuals or entities it represents. 

Adding noise to data lies in introducing random or irrelevant information to data. In case of a data breach, this method makes it difficult for malicious actors to differentiate between genuine data and randomly added data.

Pseudonymization is a de-identification method based on replacing PII with pseudonyms. While removing identifiers, pseudonymization still allows for the use of pseudonymized data for legitimate purposes.

Whatever method you choose for anonymizing data in your organization, you still may face certain problems on your way. Read on to learn about the biggest challenges in data anonymization.

Key challenges in data anonymization

Effective anonymization can act as a barrier between a business’s valuable insights and threats to personal data privacy. However, implementing effective anonymization isn’t as easy as you may think. Let’s take a closer look at the challenges organizations often face when anonymizing data:

Balancing privacy and utility

Striking a balance between data anonymization and data utility is crucial yet very challenging. On the one hand, an effective anonymization process is vital to protect the privacy of clients, employees, and other users. Thus, anonymization techniques and tools that completely erase PII from data can be highly beneficial for maintaining the privacy of the individuals you collect data from. 

On the other hand, it’s important that businesses collect and use data that carries value for research, analysis, and decision-making purposes. In fact, fully anonymized data may carry little to no value to your business, which makes data collection and processing completely irrelevant.

The ultimate goal for organizations is to achieve and maintain maximum privacy protection while keeping a sufficient level of data accuracy. Reaching that goal may require continuous evaluation and optimization of the data anonymization process. 

Preventing re-identification

Unless you’re using anonymization techniques that delete PII once and for all, there’s always a risk of anonymized data being used to track down a specific person. 

Malicious actors utilize numerous attacks to re-identify people even with anonymized data. For instance, if they manage to access anonymized data sets with financial information, they can combine it with other data sets, such as a voter registration database, and eventually perform re-identification.

Therefore, organizations must ensure the privacy of the information they collect from clients, employees, and other users. To enhance the protection of data privacy, consider combining anonymization with other data security methods. 

Complying with data security requirements

Various data protection requirements define how organizations should collect, store, and handle personal information. Some of them recommend using anonymization techniques, for instance:

• General Data Protection Regulation (GDPR) – an EU regulation that doesn’t mandate anonymization of data, but encourages the use of anonymization techniques for data protection along with other safeguards.
• California Consumer Privacy Act (CCPA) – a US law that compels organizations to anonymize collected data in order to enhance the privacy of that data. It also obliges organizations to have all the means necessary to keep data anonymized and prevent re-identification.
• Personal Information Protection and Electronic Documents Act (PIPEDA) – a Canadian law that requires organizations to protect personal information and lists anonymization as one of the data protection methods.

Each of these pieces of legislation specifies that their requirements don’t apply to fully anonymized data assets that cannot be re-identified by any means. 

If re-identification of anonymized data is possible, the requirements of these regulations and laws still apply. This means that organizations need to treat anonymized data like personal data and properly protect it.

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Data anonymization best practices

In this section, we reveal data anonymization best practices that can help you safeguard personal information while retaining the analytical value of data.

Conduct data discovery and classification

Anonymizing data is impossible when you don’t know what PII exists in your dataset. That’s why it’s necessary to identify all direct and indirect identifiers in the data you’re collecting and storing. Performing data discovery and classification can help you with that.

Data discovery aims to simplify data management. It involves the identification of all data stored by an organization, its types, and the relation between different data assets. 

In turn, data classification combines the categorization and labeling of data based on its attributes and characteristics. By dividing data into different categories, data classification makes it easier for organizations to implement security measures tailored to the specifics of various types of data.

Implementing these two practices allows you to accurately identify sensitive data that requires anonymization and make sure that no such data is left unsecured. As well, you can make informed decisions on what anonymization techniques to use and choose the ones that address the specifics of the data you need to anonymize. 

Prioritize data use cases

Unless you know exactly how people within your organization use data, you can’t take measures to protect it. Identifying all data use cases and prioritizing them helps you make your anonymization efforts more efficient. 

Consider engaging with data consumers across your organization to determine how they use data and for what purposes. It will help you reveal the most common data use cases and their importance for your business. Then, prioritize those use cases based on the risks they pose to data privacy and value for business.

With a prioritized list of data use cases, it will be easier for you to decide what sensitive information should be anonymized first. Thus, you can optimize the allocation of resources and efforts needed for anonymization.

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Map relevant legal requirements

While keeping sensitive personal information safe is the ultimate goal of anonymization, it’s also critical for your business to stay compliant with data protection requirements. Mapping laws, standards, and regulations applicable to your organization is the first step to take toward compliance.

Consider mapping applicable legal requirements in several steps:

• Identify the requirements applicable to your industry, location, and area of operation
• Study and understand the requirements
• Interpret the requirements in a way your team will understand
• Integrate the requirements into your work processes
• Document the requirements and the established procedures to meet them
• Continuously monitor if there are any changes made to these requirements and if new requirements occur
• Regularly update documentation and raise employees’ awareness of compliance measures

Besides helping you to decide on the right measures to achieve compliance, mapping relevant legal requirements also enhances your data anonymization efforts.

Minimize data collection

You may think that the more data you collect, the more accurate analyses you can conduct and the better it is for your business. However, extensive data collection may be harmful. When you collect too much data, you rarely use all of it but you still need to allocate resources to store and protect the unused data assets.

Minimizing data collection can simplify the process of data anonymization and reduce the risks to data safety. Therefore, only collect the data that is necessary for analysis and avoid collecting any data you may never use in the future. 

Assess the current technology stack

Nowadays, many platforms have built-in data anonymization functionality by default. However, you still need to evaluate if the functionality of your current technology is enough to properly anonymize personal data, prevent re-identification, and comply with data protection requirements.

Consider analyzing the anonymization capabilities of your current technology stack to check if they match the level of anonymization you want to achieve. Additionally, check whether they help you meet the data protection requirements applicable to your organization.

This process will help you determine whether the current stack is sufficient for your anonymization needs and if there are any gaps you need to cover by deploying additional data anonymization tools. 

Plan for re-identification in advance

Your organization may need to re-identify previously anonymized data for legitimate reasons. For instance, you may need it for data analysis, tailored customer support, or security incident investigation. That’s why it’s better to think of the de-anonymization process beforehand. For this, consider taking the following measures:

• Verify that your anonymization technology supports re-identification
• Define and document legitimate reasons for data re-identification
• Develop guidelines on the re-identification process and specify which techniques and tools can be used to de-anonymize data
• Assign people to be responsible and accountable for the re-identification process
• Specify the security measures to be taken to protect de-anonymized data
• Establish procedures required to limit insider access to de-anonymized data

By planning for data re-identification ahead of time, you reduce the likelihood of breaching data privacy while ensuring accessibility to data when there’s a need for it.

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Ekran System’s Anonymizer for personal data protection

As an insider risk management and user activity monitoring platform, Ekran System records all user activity within your infrastructure. Logging and recording all user sessions helps you keep an eye on user behavior, and timely notice indicators warn you of account compromise or insiders’ malicious intent. 

However, continuous user activity monitoring poses severe risks to the privacy of users’ personal information. Suppose a malicious actor happens to access logs and session recordings. In that case, they could eventually discover your employees’, vendors’, or partners’ identities and use them to carry out activities such as fraud, identity theft, or cyberbullying. 

To prevent such scenarios, Ekran System provides the possibility to anonymize monitored data. Ekran System allows you to remove all identifiers from monitored data and prevent exposing personal data. Depending on the type of personal data, Ekran System’s anonymization is achieved by either randomizing, hiding, or obfuscating.

At Ekran System, we understand how crucial monitored data is for security incident investigations. That’s why administrators and others with access to user sessions in Ekran System can request that data be de-anonymized. Upon a supervisor’s approval, the de-anonymized data will become temporarily visible.

With Ekran System’s Anonymizer, you can ensure the safety of users’ PII while being able to retrieve it whenever an incident occurs. Thus, you can anonymize all data by default, but de-anonymize data whenever you receive an alert on suspicious activity or detect potentially malicious actions during a security audit. As soon as you de-anonymize a specific user’s data, investigators get access to that user’s original personal data and can perform a thorough security incident investigation without putting other users’ privacy at risk.

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Conclusion

Collecting and processing data can benefit your business, but you should never forget the risks that come with it. Although anonymization of data isn’t a silver bullet against all data security issues, it still enhances the safety of personal data in your organization. 

By adhering to the best practices we’ve discussed in this article, you can protect data privacy while preserving the ability to use it for analysis or security incident investigation. Yet you need to have robust technological solutions to implement anonymization properly.

Ekran System is an insider risk management platform that not only minimizes insider risks to your organization but also allows you to ensure the privacy of personal data.

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*** This is a Security Bloggers Network syndicated blog from Ekran System authored by [email protected]. Read the original post at: https://www.ekransystem.com/en/blog/data-anonymization-best-practices