What is Data De-Identification, and Why do I Need it?

Data is necessary for testing, research, and algorithm training. However, privacy regulations and data security protocols mean that companies can’t just run these on their gathered data. Businesses risk high financial losses and reputational damage if such data leaks. This is where de-identified data comes into play.

By de-identifying data, you remove direct and indirect identifiers from the datasets, making it impossible to track the information back to individuals. However, manual de-identification is time-consuming and error-prone. Companies can use automation tools, but not all offer equal levels of privacy. Even worse, certain techniques decrease data usability, making it less fit for purpose.

Our article will discuss the most popular techniques of data de-identification and explain how to maintain the quality of the datasets. But first, let’s start with the definition of the de-identified data.

What is data de-identification?

Data de-identification means removing, masking, or replacing sensitive personally identifiable information (PII) from data. This process allows companies to comply with privacy regulations when they use data for testing, analytics, research, and so on.

PII can reveal sensitive data directly and indirectly via identifiers. Direct identifiers point to an individual and can include the following:

• Full names
• Unique identifying numbers (Social Security number, certificate, license number, etc.)
• Passport scans
• Location data 
• Biometric identifiers (fingerprint, voice sample, face ID, etc.)
• Trade union membership cards
• Protected health information (medical records, treatment history, etc.)

Indirect identifiers can be used to identify an individual when combined with other information. Examples of this data include: 

• Date of birth
• ZIP code
• Contact information (email address, telephone or fax number, web URL, etc.) 
• IP addresses
• Vehicle information
• Gender identity
• Educational information
• Product serial numbers
• Transaction history
• Employment data
• Private communications (correspondence)

Dealing with identifiers is quite a challenge, given the volume of information organizations collect daily.

Sensitive information is found across industries

Businesses in all sectors accumulate indirect information that could reveal someone’s identity and, consequently, break privacy laws. Here are a few examples:

• Finance companies store account identifiers, credit card numbers, and customers’ spending patterns.
• Healthcare providers collect information about health conditions, treatments, and insurance details.
• Marketers work with information about purchase histories and a wealth of demographic information.
• Manufacturers record employee details, supplier information, production output, and maintenance logs.
• Logistics and transportation companies keep customer delivery addresses, payment data, and driver details.

As the volume of PII data increases, so do the risks associated with non-compliance, which is why many businesses are increasingly investing in de-identification tools.

Why should you de-identify data?

Data de-identification allows companies to use, share, and sell high-quality data freely. Let’s break down all the benefits: 

• Adherence to data privacy laws: Data privacy regulations mandate the rules for collecting, storing, sharing, and managing PII. Data de-identification and anonymization are necessary to comply with strict data privacy regulations. These regulations, such as GDPR in the EU and CCPA in California, impose severe penalties for non-compliance. By ensuring your data is de-identified, you can avoid hefty fines and maintain your operations without legal interruptions. Moreover, regulatory compliance is not just about avoiding penalties but also about fostering a culture of respect for user privacy, which can enhance your company’s reputation.
• Reduced costs of compliance: With automated de-identification software, you can apply standardized techniques consistently across your datasets. This further reduces the costs of privacy compliance and risks of financial penalties for regulatory violations. Automated solutions minimize the need for extensive manual oversight and labor, thereby cutting down operational costs. Standardized techniques also ensure that your compliance measures are uniformly applied, reducing the risk of human error, which can lead to costly breaches and compliance failures. Investing in automated de-identification tools can thus provide significant long-term savings.
• Lowered impact of data breaches: The cost of an average data breach has risen from $3.62 million in 2017 to $4.45 million in 2023 (according to an IBM report). By de-identifying your datasets, you will greatly reduce the potential harm if attackers gain access to your databases. De-identified data is less valuable to cybercriminals because it lacks the personal identifiers that are often the target of attacks. Even if a breach occurs, the impact is minimized, as the compromised data does not reveal personal information. This reduction in potential harm also translates into lower legal and remediation costs following a breach, further protecting your company’s financial health.
• Extra protection of PII: You can prevent unauthorized access to and malicious misuse of data by using only de-identified datasets. It’s important to note that the risk is from malicious agents and those who handle data daily — such as software developers, testers, data analysts, and service providers — who can unintentionally compromise data. By de-identifying data, you mitigate the risks associated with insider threats and accidental data exposure. This practice not only safeguards sensitive information from external attacks but also from internal mishandling. It creates a safer data environment, ensuring that PII is protected at all stages of data handling and processing.
• Improved operational efficiency: By reducing the need for extensive protection measures, you facilitate access to the data for your employees. A reliable process actually speeds up product development cycles, research, and business operations. With fewer restrictions on data usage, employees can access and utilize data more freely and efficiently, leading to faster innovation and quicker decision-making processes. This increased accessibility can significantly enhance productivity and enable more agile and responsive business operations, ultimately providing a competitive edge in the market.
• Elevated customer trust: Customers are more likely to use your services and share their personal information if your reputation isn’t tarnished by data leaks, civil lawsuits, and compliance fines. Building and maintaining customer trust is crucial for long-term success. When customers know that their data is handled responsibly and securely, they are more inclined to engage with your services and products. This trust can lead to increased customer loyalty, higher retention rates, and a better overall brand image. 
• Enhanced sharing and collaboration: De-identification allows you to safely share data with your employees, business partners, and other third parties without breaking privacy regulations. This capability is particularly valuable for collaborative projects, partnerships, and research initiatives that require data sharing. De-identified data can be shared across different departments and organizations with reduced chances of compromising privacy, enabling more effective and cooperative efforts. It also enhances compliance with data-sharing agreements and the overall quality of collaborative outputs.
• Additional revenue streams: You can use de-identification tools to build a data marketplace for monetization purposes. Many businesses will pay for high-quality data for testing, AI algorithm training, or research. For example, thanks to our platform, Erasmus Medical Center sells synthetic data to healthcare and medical research companies. Creating a data marketplace not only opens new revenue opportunities but also maximizes the value of your data assets. By providing de-identified datasets, you can cater to various industries’ needs for data-driven insights. This diversification of revenue streams can significantly boost your company’s financial performance and resilience.

Because privacy requirements differ in certain jurisdictions and sectors, de-identification tools have to meet several regulations. Advanced de-identification solutions are designed to accommodate varying legal standards and offer customizable options to ensure compliance across different regions.

Regulatory requirements for data de-identification

Most privacy regulations contain requirements similar to those in GDPR, CCPA, CPRA, and HIPAA. The data protection laws with which you must comply will depend on the location of your business and the residency of your users: 

• General Data Protection Regulation: The GDPR is a comprehensive data protection law in the European Union (the United Kingdom follows the UK-GDPR). Companies must comply with it if they collect, store, share, and process the personal data of EU (or UK) citizens. 
• Health Insurance Portability and Accountability Act: HIPAA regulates health information management for US healthcare providers, clearinghouses, and business providers. Specifically, the HIPAA (Privacy Rule) sets forth the conditions for using, disclosing, and retaining individual information. It also sets out permissible methods of de-identifying data, such as the Safe Harbor or Expert Determination methods. 
• California Privacy Laws: The California Consumer Privacy Act (CCPA) and the California Privacy Rights Act (CPRA) establish additional privacy rules regarding collecting and using California residents’ PII.

All businesses and entrepreneurs must comply with these strict obligations when dealing with any information. However, GDPR, HIPAA, and California Privacy Laws exclude de-identified data. That’s right. Any dataset that lacks information that is traceable back to an individual falls outside the scope of these regulations. 

To ensure your data remains non-regulated, you must employ de-identification methods that remove the PII in a way that makes it impossible for individuals to re-identify the data.

How to de-identify data

We follow the Safe Harbor method to guarantee compliance with de-identified datasets. This method requires you to remove or modify all direct and indirect identifiers—18 types in total.  Here’s how organizations can establish a continuous data de-identification process:

1. Organize the data

De-identification begins with thoroughly auditing all applications, databases, and tables. You should understand what data is collected, how it’s stored, and how long it’s retained. Create a map of all data sources and their flow within your organization. 

At this point, stakeholders should be assigned ownership of specific types of data to ensure accountability. Conduct audits regularly to maintain compliance.

2. Detect PII in datasets

Identify all datasets that contain PII and other sensitive data. Next, you should classify this data into different groups, such as non-sensitive data, direct and indirect identifiers, corporate information, and compliant data. 

To streamline the management, companies also establish policies for identifying and handling PII. For extra security, apply access control rules to different types of data based on regulatory requirements and business needs.

3. Tag identifiers

Once data is classified, it should be tagged with appropriate metadata to indicate its sensitivity and type. Implement standardized tagging conventions to ensure uniformity across all datasets and streamline the de-identification process.

4. Select the de-identification method

Select the de-identification technique based on your needs, such as the data utility requirements and regulatory rules. The techniques vary in terms of privacy protection and have different impacts on usability. 

For instance, the pseudonymization technique replaces PII with pseudonyms or codes while barely affecting data structure. However, in experienced hands, this information can be re-identified. More advanced tools can replace sensitive data without compromising privacy or usability.

You may de-identify data on database and column levels.

Database-level de-identification

For database-level de-identification, simply drag tables from your relational database into the de-identify section in the workspace.

Column-level de-identification

To apply de-identification on a more granular level or column level, open a table, choose the specific column you want to de-identify, and effortlessly apply a mocker. Streamline your data protection process with our intuitive configuration features.

5. De-identify datasets

Apply the selected de-identification techniques to the selected datasets. De-identification should be viewed as an iterative process rather than a one-time task. We recommend picking a few sample datasets. After the initial de-identification, you should review the results before proceeding.

6. Validate the results

You should assess the de-identified data to ensure it meets your business requirements. It’s necessary to engage the data owners and other relevant stakeholders in the review. The validation process itself should involve several steps:

• Verify that all identifiers were removed or replaced.
• Evaluate the possibility of re-identification based on a combination of remaining data points.
• Confirm that the de-identified information retains the original level of detail and accuracy.
• Make sure no critical records or files are lost or corrupted.
• Check if the relationships and patterns within the data are preserved.

As you can expect, doing all this manually is tedious, long, and expensive. Besides, doing this by hand will result in occasional errors and inconsistencies, which increase the identification risk. That’s why organizations use automated de-identification methods.

Smart data de-identification

The truth is that most techniques of PII removal leave vulnerabilities that malicious actors can exploit to trace the data back to the individuals. Other methods decrease the statistical accuracy of data to the point that they can’t be used for advanced research and AI training. 

Syntho’s smart de-identification technology is made to automate manual work without privacy or quality trade-offs. Our advanced AI-powered scanner identifies PII across tables, databases, and other sources. 

Once identified, the platform replaces the sensitive information with mock data. At the same time, our engine maintains a consistent mapping to preserve referential integrity and business patterns.

That isn’t all. Our software has extra features that might enhance the de-identification process:

• Data enrichment allows adding rows and columns to the de-identified datasets, making it easier to create larger and more comprehensive testing datasets.
• Subsetting helps create smaller datasets for testing, reducing the burden on storage and processing resources.
• Rule-based flexibility makes the data adaptable to different data formats, structures, and scenarios.
• Data cleansing corrects inconsistencies, fills in missing values, and removes corrupted data.

Syntho automates most of the manual work, lowers the chances of missing sensitive data, and maintains the quality of the original data.

Automated data de-identification at scale

De-identification is necessary to comply with privacy regulations, protect sensitive information, and maintain data usability. Removing or masking identifiers can improve operational efficiency, lower security risks, and even decrease operational costs. However, manual de-identification is far too inefficient for most businesses.

Syntho’s smart de-identification technology automates the de-identification of PII across datasets. It uses AI to detect sensitive information and replaces it with mock data based on your business rules, all while maintaining the original quality of the data.

Do you want to improve your de-identification process and ensure compliance? Contact us to get a demo.