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Data Privacy 14 min read

GDPR Data Masking Best Practices: A Shield for Personal Information

GDPR Data Masking Best Practices: A Shield for Personal Information

In the modern era of digitalization, safeguarding both personal and non-personal information has become essential. Adherence to regulatory standards like the General Data Protection Regulation (GDPR) is fundamental, particularly for businesses delving into the complex realms of artificial intelligence (AI) and machine learning (ML). This is precisely where data masking plays a pivotal role. Among the array of techniques designed to protect data while ensuring legal compliance, data masking emerges as a highly effective safeguard.

In this article:

  • The GDPR and data masking
  • What is data masking?
    • How it works and why it is important
    • What type of data can be masked?
  • The art of data masking: techniques and methods
    • Data masking use cases
    • Types of data masking
    • Data masking techniques
    • Implementing GDPR data masking best practices
  • The relationship between data masking and differential privacy
  • Data masking pros and cons
    • Benefits of data masking
    • How data masking techniques affect data quality
    • Challenges of implementing data masking techniques
    • A case study
  • Conclusion

The GDPR and data masking

Various data protection standards and regulations mandate the safeguarding of personally identifiable information (PII) and sensitive data such as health information. Among these standards and regulations are the GDPR, the California Privacy Rights Act (CPRA), the Health Insurance Portability and Accountability Act (HIPAA), and the Payment Card Industry Data Security Standard (PCI DSS).1 The GDPR has significantly influenced the handling of organizational data by imposing stringent measures to safeguard individuals’ privacy rights within the European Union.

The GDPR does not explicitly mandate data masking but emphasizes the importance of implementing appropriate technical and organizational measures to protect personal data. Also, the GDPR recognizes pseudonymization (a form of data masking) as a recommended safeguard for protecting personal data. Additionally, the regulation encourages using techniques like encryption, another form of data masking. Hence, data masking is widely recognized as a valuable technique that can help organizations comply with several key principles and requirements of the GDPR.2

It is also important to note that while regulations are crucial for establishing adequate data protection levels and preventing unauthorized access, they also pose challenges for companies seeking to analyze or share their data. Data masking serves, finally, as a vital tool to mitigate the risks of data exposure, enabling enterprises to comply with various standards and regulations while handling regulated data3.

What is data masking?

How it works and why it is important

Data masking, also known as data obfuscation or anonymization, involves protecting information in databases or other data storage systems by replacing original data with fictional or scrambled data. While masked data retains the format and appearance of the original, it lacks intrinsic value, rendering sensitive information unreadable and unusable to unauthorized individuals. However, it still permits data utilization for development, testing, or analysis purposes.

For example, consider a scenario where a healthcare organization intends to share medical records, including patient names, social security numbers, and medical diagnoses, for research purposes while adhering to the GDPR. To facilitate such collaborations while safeguarding patient privacy, the organization opts for generalization, a data masking technique. Instead of disclosing specific medical conditions such as type 2 diabetes, the organization generalizes the information to indicate a chronic condition. This approach reduces data granularity while preserving statistical relevance, thus safeguarding sensitive medical information while enabling valuable research insights.

What type of data can be masked?

Data masking is selectively applied to specific types of data stored within companies. Some common data types that are typically protected through data masking are4:

  • Personally identifiable information (PII) includes any information that can be used to identify an individual, such as names, addresses, phone numbers, email addresses, passport numbers, and social security numbers.
  • Protected health information (PHI) encompasses a broad range of medical data, including patient medical records, health insurance information, prescription details, medical test results, and demographic information related to patients.
  • Financial information, such as credit card numbers, bank account details, and financial transaction records, is highly sensitive and requires protection to prevent fraud or unauthorized access.
  • Intellectual property (IP) includes valuable assets such as inventions, designs, proprietary algorithms, or any information that holds significant value to the organization. Safeguarding intellectual property is crucial to prevent theft or unauthorized use.

By applying data masking techniques to these types of data, companies can achieve several objectives:

  • Protecting privacy: data masking helps safeguard individuals’ privacy by preventing unauthorized access to sensitive personal information.
  • Compliance with regulations: as we have seen above, data masking assists organizations in complying with data privacy regulations such as the GDPR, HIPAA, CPRA, and PCI DSS, all of which mandate the protection of sensitive data5.
  • Reducing risk: by obfuscating sensitive data, organizations can reduce the risk of unauthorized access, exposure, or misuse of confidential information, thereby enhancing their overall data security posture.

The art of data masking: techniques and methods

Data masking use cases

Data masking can be used in many industries to improve services and drive innovation. For example, the banking and finance industries use it to develop and test new systems and enhance fraud detection algorithms. The healthcare industry uses it to protect data and gain new insights.

Below are some of the most common use cases:

  • Data breach prevention: data masking techniques act as a primary defense mechanism against data breaches. Even if network defenses falter and attackers manage to access sensitive data, effective masking or encryption renders the data unintelligible to unauthorized users. This ensures that even in the event of a breach, attackers cannot associate the data with individuals, thus mitigating the breach’s impact.
  • Faster, safer test data: masked data retains the integrity and quality necessary for testing purposes without compromising the actual data.
  • Privacy by design: incorporating data masking into the application development, testing, and analysis lifecycles enables the sharing of essential data both internally and externally while ensuring compliance with stringent data privacy regulations. By implementing data masking proactively, companies can embed privacy into their processes from the outset, thereby promoting a culture of privacy by design.
  • Role-based access control: many organizational tasks necessitate access to specific data attributes while restricting access to others. Data masking facilitates role-based access control by ensuring employees can fulfill their duties without accessing data they are not authorized to view. By selectively masking sensitive information based on employees’ roles and access privileges, companies can uphold data privacy and security while enabling efficient task execution.

In essence, data masking serves as a linchpin in safeguarding sensitive information, facilitating secure testing environments, ensuring regulatory compliance, and enabling controlled access to data within organizations. Its adoption across various industries underscores its indispensable role in preserving data security and privacy in today’s data-driven landscape.

Types of data masking

There are three primary types of data masking6:

  • Static data masking involves replacing sensitive data with masked or fictitious data in non-production environments, maintaining consistency over time. This technique is ideal for scenarios where data consistency is crucial. For instance, a financial institution utilizes static data masking to conceal actual credit card numbers in its customer transaction data for analytical purposes. Replacing the first 12 digits with “X” characters while retaining the last 4 digits unchanged obscures sensitive credit card information, ensuring compliance with data protection regulations while facilitating secure storage and analysis.
  • Dynamic data masking (DDM) alters sensitive data in real time based on the user’s access privileges, ensuring that unauthorized users only see masked or partial information. For example, an online retail platform implements dynamic data masking to restrict unauthorized access to customer email addresses. Unauthorized users view partially masked email addresses, while authorized users with appropriate access privileges can access the complete, unmasked data. This approach enhances data security by limiting access to sensitive information while allowing authorized users to fulfill their roles effectively.
  • Tokenization replaces sensitive data with unique tokens (randomly generated strings of characters) to enhance security and minimize the risk of data breaches. For instance, a payment processing company implements tokenization to replace actual credit card numbers with tokenized representations. The original credit card number, “1234 5678 9012 3456,” is replaced with a tokenized representation, “ABCD1234EFGH5678”, ensuring secure transaction processing without exposing sensitive information to unauthorized access7. By leveraging tokenization, the company strengthens data security, complies with data protection regulations such as PCI DSS, and safeguards customer information during transactions.

Data masking techniques

Effective data masking encompasses a range of techniques aimed at safeguarding sensitive information while maintaining data usability. Here are some common data masking practices89:

  • Substitution replaces sensitive data with fictional data while preserving the format and structure of the original information. This technique is valuable in test environments where data integrity is critical for development and quality assurance.
  • Encryption utilizes algorithms and keys to transform data into an unreadable format, ensuring only authorized parties can decrypt and access the original information. Encryption secures data at rest and in transit, preventing unauthorized access.
  • Shuffling or data permutation randomly rearranges values within a dataset, making it challenging to identify specific individuals or information. This technique balances data utility with privacy protection.
  • Redaction selectively removes or obscures sensitive information from documents or records, commonly used in legal and government contexts to protect confidential data.
  • Nulling out replaces sensitive data with null values (e.g., empty fields or placeholders), which is particularly useful when preserving the data structure is not essential.
  • Masking algorithms: sophisticated algorithms transform sensitive data into an unreadable format, often reversible for authorized users to restore the original data.
  • Format-preserving encryption (FPE) balances data security and utility, which is particularly challenging in healthcare, by protecting patient privacy while allowing data access for medical research.
  • Scrambling rearranges characters and integers within a data field in a random order, restricted to specific data types.
  • Number Variance: applicable to financial information, masks original values by applying a percentage variance.
  • Date aging increases or decreases dates by a specific range while maintaining application constraints, which is useful for aging contracts or other time-sensitive data.
  • Averaging replaces original data values with an average, ensuring data privacy while retaining statistical relevance.

These data masking techniques play a critical role in safeguarding sensitive information and ensuring compliance with data privacy regulations like the GDPR. By implementing these practices, organizations can protect privacy, prevent data breaches, and maintain data integrity.

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Implementing GDPR and data masking best practices

Implementing effective data masking strategies requires a nuanced approach, balancing the imperatives of privacy protection with the demands of data usability. Here are some best practices to guide data masking endeavors10:

  • Identify sensitive data: conduct a comprehensive assessment to identify sensitive data elements subject to the GDPR.
  • Utilize masking techniques: employ various masking techniques, including substitution, shuffling, and encryption, to obfuscate sensitive information effectively.
  • Preserve data utility: ensure that masked data remains suitable for intended use cases, preserving its analytical value while safeguarding privacy.
  • Implement access controls: restrict access to masked data based on user roles and permissions, preventing unauthorized exposure.
  • Regularly audit and update: continuously evaluate and update data masking policies to adapt to evolving threats and regulatory requirements.

The relationship between differential privacy and data masking

Data masking and differential privacy are complementary techniques that work in tandem to safeguard sensitive data, with data masking providing the initial anonymization and differential privacy adding an additional layer of protection through controlled noise or randomization.

The two techniques work well together, providing robust data privacy protection:

  • Data masking serves as the first line of defense, anonymizing data by removing or altering personal identifiers.
  • Differential privacy then adds an extra layer of protection by introducing controlled noise or randomization to the masked data.

This combination ensures that even if the masked data is compromised, the addition of differential privacy makes it extremely difficult to re-identify individuals or extract their personal information from the dataset11.

Furthermore, differential privacy enables companies to analyze data and extract valuable insights from masked and randomized data while still providing strong privacy guarantees. This is particularly important in fields such as healthcare, where organizations need to leverage sensitive patient data for research or analytics purposes while maintaining strict privacy standards.

By combining data masking and differential privacy, organizations can strike a balance between protecting individual privacy and unlocking the potential of data-driven insights. This comprehensive approach aligns with the principles of data protection regulations like the GDPR and helps organizations maintain compliance while fostering innovation and trust.

Data masking pros and cons

Benefits of Data Masking

Data masking practices offer numerous benefits for companies striving to safeguard sensitive information. Some of these advantages include12:

  • Compliance with regulations: data masking helps businesses adhere to regulations by ensuring that only authorized personnel can access and view actual sensitive data, thereby avoiding potential penalties and legal consequences.
  • Data privacy: customers expect their data to be handled securely when entrusting it to a business. Data breaches not only jeopardize sensitive information but also erode trust and tarnish a company’s reputation. Data masking maintains customer trust by minimizing the risk of data exposure, protecting individuals’ privacy, and preventing unauthorized access to personal information.
  • Data utilization: data masking enables businesses to use realistic data for testing and development purposes without compromising actual sensitive information. This practice is crucial for ensuring the functionality and accuracy of software and systems while adhering to data privacy regulations.
  • Enhanced security: by concealing sensitive data, data masking helps mitigate the risk of data breaches, malware, and cyberattacks. It adds an extra layer of security, making sensitive information less appealing to cybercriminals and significantly reducing the likelihood of unauthorized access.
  • Secure third-party data sharing: in scenarios where businesses need to share data with third parties, data masking ensures that the shared data does not expose sensitive details. This facilitates secure data sharing and fosters partnerships while safeguarding confidential information.
  • Cost-efficiency: data breaches can incur significant costs in terms of legal fines, reputational damage, and remediation efforts. Data masking minimizes the risk of breaches, resulting in cost savings for organizations by avoiding potential financial losses associated with data breaches and regulatory non-compliance.

Overall, data masking serves as a valuable tool for companies to protect sensitive information, maintain compliance with regulations, uphold customer trust, and enhance overall cybersecurity posture, all while promoting efficient data utilization and secure data sharing practices.

How data masking techniques affect data quality

Each data masking technique has advantages and disadvantages, and the choice of method should align with the specific requirements and sensitivity of the data being protected13. Furthermore, data masking techniques can have both positive and negative impacts on data quality. Here’s how these techniques might affect data quality:

  • Potential loss of detail: depending on the masking technique used, there is a risk of losing specific details or precision in the original data, which can impact the accuracy of analysis or reporting.
  • Impact on data analysis: some masking methods, such as shuffling, may disrupt patterns or relationships in the data, affecting the outcomes of certain analytical processes that rely on specific data structures.
  • Risk of de-identification: improper implementation of data masking techniques can lead to re-identification risks, where individuals can be identified from masked data through inference or linkage attacks (see also our article on “AI Re-Identification Attacks”).

While data masking techniques play a crucial role in protecting sensitive information and ensuring data privacy, companies must carefully balance the trade-off between privacy and data quality (see also our article on “The Most Common Data Anonymization Techniques”). By selecting appropriate masking methods and implementing them effectively, companies can mitigate risks to data quality while enhancing security and compliance with regulatory requirements.

Challenges of implementing data masking techniques

Implementing data masking techniques comes with various challenges that companies need to address to ensure the effective protection of sensitive information. Here are some common challenges14:

  • Test data requirements: companies may face challenges in determining the appropriate test data requirements for data masking, ensuring that the masked data accurately reflects real-world scenarios while protecting sensitive information.
  • Data sensitivity: assessing the sensitivity of each data set and identifying the level of protection required can be a challenge in implementing data masking effectively. Different datasets may require varying degrees of masking based on their sensitivity.
  • Data mitigation: managing data mitigation processes, including identifying and addressing potential risks associated with data masking, can pose challenges for organizations seeking to protect sensitive information while maintaining data utility.
  • Hindering productivity: certain data masking techniques may lead to delays in data processing, impacting productivity within organizations. Balancing the need for data protection with operational efficiency is crucial to avoid unnecessary slowdowns in workflows.
  • Complexity of implementation: implementing data masking techniques successfully requires a deep understanding of the company’s data landscape, potential vulnerabilities, and the most suitable masking methods for different types of sensitive information. This complexity can present challenges during implementation.
  • Resource intensive: data masking processes can be resource-intensive, requiring significant computing power and time to effectively mask large volumes of data. Companies may face challenges in allocating resources and managing the computational demands of data masking.

By addressing these challenges proactively and adopting best practices in data masking implementation, companies can enhance their data privacy efforts, protect sensitive information, and ensure compliance with regulatory requirements.

A practical example: implementing data masking in financial services15

Based on our analysis, we offer a practical illustration of a real-world data masking endeavor. We explore the challenges companies face dealing with data and how data masking can effectively address them.

In the financial industry, companies often operate globally, encountering significant hurdles in safeguarding sensitive customer data while meeting stringent regulatory standards such as the GDPR in the EU and CPRA and PCI DSS in the USA16. With data scattered across diverse databases and systems spanning various locations, these firms must prioritize two main objectives: implementing robust data masking solutions to protect customer information and preserving data usability for internal analysis and operations. Ensuring data privacy and compliance is essential for maintaining trust and credibility in the market.

When undertaking data masking initiatives, companies aim to:

  • Develop a comprehensive data masking strategy covering all systems and databases
  • Implement automated processes to streamline data masking and ensure consistency
  • Ensure compliance with regulatory frameworks

However, companies encounter several challenges during the implementation of data masking:

  • Diverse data sources: data is stored across complex ecosystems of databases, applications, and third-party systems, each containing different types of sensitive data such as PII, financial transactions, and account details.
  • Regulatory compliance: adhering to GDPR and PCI DSS requires robust controls to protect customer data from unauthorized access or disclosure.
  • Data usability: while prioritizing data security, companies must ensure that masked data remains usable for legitimate business purposes, including analytics, reporting, and customer service.

To successfully execute a data masking initiative, companies should develop a solution comprising the following key components:

  • Data discovery and classification: conduct a comprehensive assessment of all data sources to identify sensitive elements and classify data based on regulatory requirements.
  • Masking techniques and policies: utilize a range of masking techniques such as substitution, shuffling, and encryption while establishing policies to govern data handling and ensure consistency.
  • Monitoring and compliance: establish robust monitoring and auditing mechanisms to track data access, masking activities, and compliance with regulations. Regular audits should be conducted to assess the effectiveness of the data masking solution and identify areas for improvement.

By prioritizing data protection and leveraging effective masking processes, companies can demonstrate their commitment to safeguarding customer information and upholding trust and integrity in the financial services industry.

Conclusion

As technology continues to evolve, the landscape of data masking and privacy protection will also transform. Emerging trends and challenges in this space include advancements in AI and ML, where more sophisticated algorithms may pose new hurdles in preserving data privacy and preventing re-identification of masked data (see our article “AI Re-identification Attacks”). Moreover, governments and regulatory bodies are likely to impose stricter data privacy laws and guidelines, necessitating organizations to remain vigilant and adapt their data masking strategies accordingly. Finally, with organizations engaging in more data-sharing practices (see also article “The Data Act and PPT”), ensuring secure and compliant data masking across distributed systems will become increasingly important. Data masking ensures the protection of data in non-production environments, facilitating secure sharing with third-party contractors and aiding in regulatory compliance efforts. By adhering to best practices outlined in this article and remaining vigilant to the constant developments of data masking, companies can confidently navigate the GDPR regulatory landscape, instilling trust among stakeholders while unlocking the transformative potential of their data.

 

1 Michael Cobb, “Data Masking,” TechTarget, https://www.techtarget.com/searchsecurity/definition/data-masking
2 Here’s what the GDPR says about data masking, either directly or indirectly:
3 Id as note 1
4 Nadejda Alkhaldi et al, “What is data masking and how to implement it?”, Itrexgroup, https://itrexgroup.com/blog/what-is-data-masking-and-how-to-implement-it-the-right-way/
5 Id as note 1
6 Anas Baig, “Data masking Best Practices and Benefits,” 27 October 2023, Dataversity, https://www.dataversity.net/data-masking-best-practices-and-benefits/
7 Id as note 6
8 Id as note 4
9 Linda Rosencrance, “Data Masking Techniques and Best Practices,” TechBeacon, https://techbeacon.com/enterprise-it/data-masking-techniques-best-practices
10 Id as note 6
11 “How Differential Privacy Complements Anonymization To Ensure Data Security, ” 3 October 2022, Pangeanic, https://blog.pangeanic.com/how-differential-privacy-complements-anonymization
12 Id as note 4
13Here is a list of some of the disadvantages for some data masking techniques:

Data Encryption:

  • Can be resource-intensive, impacting system performance
  • Key management complexities may arise
  • May not fully protect against insider threats

Scrambling:

  • May not provide strong protection against sophisticated attacks
  • Could impact data quality if not implemented carefully
  • Limited effectiveness in certain scenarios

Substitution:

  • Risk of re-identification if not done meticulously
  • Potential loss of specific details in the original data
  • Requires careful selection of realistic substitute values

Shuffling:

  • May impact data analysis that relies on specific order or relationships
  • Could introduce errors if not implemented correctly
  • Limited effectiveness for certain types of data

Number & date variance:

  • Risk of de-anonymization through statistical inference techniques
  • Potential loss of precision in numerical or temporal values
  • Requires careful calibration to balance privacy and utility

14 “Data Masking: Concealing Sensitive Information for Privacy Protection,” Faster Capital, https://fastercapital.com/content/Data-masking–Concealing-Sensitive-Information-for-Privacy-Protection.html#Challenges-in-Implementing-Data-Masking.html
15 This case study is drawn from a study carried on by Itrexgroup, www.itrexgroup.com
16 Id note 1


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