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Data Pseudonymization

In today’s data-driven world, organizations collect and store vast amounts of sensitive information in databases and data warehouses. With the increasing focus on data privacy and security, it is crucial to implement effective measures to protect this data from unauthorized access and misuse. One such technique is pseudonymization, which has gained significant attention in recent years.

What is Pseudonymization?

Pseudonymization is a data protection technique that involves replacing personally identifiable information (PII) with a pseudonym. Pseudonym is a unique identifier which links the changed data back to the original data. The objective of this data protection is to minimize the likelihood of data leaks. This protects the privacy of individuals whose information the database contains.

The term “pseudonymization” derives from the Greek words “pseudes” (false) and “onoma” (name), which together mean “false name.” This describes how individuals can replace real identities with fake ones. But authorities can still identify the data when needed.

What’s the difference with masking?

Data masking and pseudonymization are both techniques used to protect sensitive data, but they serve slightly different purposes and have distinct characteristics:

Data Masking

Purpose: The primary purpose of data masking is to hide original data with modified, yet realistic, data. It’s typically used in non-production environments where real data is not necessary, such as during software testing or for analytical purposes.

Technique: Data masking involves replacing sensitive data with fictional or scrambled data while preserving the format and characteristics of the original data. Common techniques include substitution (e.g., replacing names with generic placeholders), shuffling (randomly rearranging data), and encryption.

Example: In a database used for testing, credit card numbers may be replaced with dummy credit card numbers that follow the same format but are not real.


Purpose: Pseudonymization involves replacing identifying information with pseudonyms or artificial identifiers. Its primary purpose is to de-identify data, making it more difficult to attribute to a specific individual without additional information.

Technique: Unlike data masking, which often retains the format of the original data, pseudonymization typically involves replacing identifying information with irreversible tokens or unique identifiers. It aims to prevent re-identification while still allowing data to be used for certain purposes, such as research or analytics.

Example: In a medical database, patient names and social security numbers may be replaced with unique identifiers, making it more difficult to link the data to specific individuals without access to a separate mapping table.

Benefits and other masking techniques

The benefits of masked data are following:

  1. Enhances data privacy and security
  2. Reduces the risk of data breaches
  3. Allows for data processing without exposing sensitive information
  4. Helps organizations comply with data protection regulations such as GDPR

Pseudonymization is often confused with other data masking techniques. For example, there are anonymization and encryption. However, there are key differences between these methods:

  1. Anonymization: This technique involves irreversibly removing all personally identifiable information from the data, making it impossible to trace back to the original individual. You cannot re-identify Anonymized data back.
  2. Encryption: Encryption is the process of converting plain text into a coded format using a key. While encryption provides a high level of security, it does not necessarily protect the privacy of individuals, as the encrypted data can still be linked back to the original data if the key is compromised.

Implementing in Databases

To implement pseudonymization in a database, you can follow these steps:

  1. Identify the sensitive data fields that need to be pseudonymized, such as names, email addresses, or social security numbers.
  2. Create a function that generates unique replacements for each sensitive data value. This function should be deterministic, meaning that it always generates the same result for a given input value.

Example: function in SQL

RETURN SHA2(CONCAT('secret_key', value), 256);
Apply the function to the sensitive data fields in your database.
Example for a table:
UPDATE users
SET name = pseudo(name),
email = pseudo(email),
ssn = pseudo(ssn);

Store the mapping between the original values and their pseudonyms in a separate, secure location. This mapping is necessary for re-identification purposes when authorized.

Implementing in Data Warehouses

You can also apply pseudonymization in data warehouses to protect sensitive information. Warehouse users can apply this throughout the process of examining and presenting data. The process is similar to that of databases, but with a few additional considerations:

  1. Identify the sensitive data fields in the source systems that feed into the data warehouse.
  2. Obfuscate the sensitive data fields during the ETL (Extract, Transform, Load) process.  Conceal all PII before importing the data into the data warehouse.
  3. Ensure that the pseudonymization function is consistent across all source systems and the data warehouse. This will help maintain the accuracy of the data for analysis purposes. Ensuring uniformity of the pseudonymization function will maintain the reliability of the data and enable effective analysis.
  4. Implement access controls and monitoring mechanisms to prevent unauthorized access to the pseudonymized data and the mapping between pseudonyms and original values.

Example with a bash script

function pseudo() {
echo "$1" | sha256sum | cut -d ' ' -f 1
# Read sensitive data from source file
while IFS=',' read -r name email ssn; do
# Apply function
# Write hidden data to output file
echo "$pseudo_name,$pseudo_email,$pseudo_ssn" >> pseudonymized_data.csv
done < source_data.csv


Pseudonymization is a powerful technique for protecting sensitive data in databases and data warehouses. By replacing personally identifiable information with unique pseudonyms, organizations can enhance data privacy and security while still enabling data processing. Pseudonymization differs from other masking techniques like anonymization and encryption in terms of its ability to re-identify data when necessary.

When implementing pseudonymization, it is crucial to adhere to best practices. Implementing pseudonymization requires following best practices. This means using a deterministic pseudonymization function. It also involves securely storing the mapping between pseudonyms and original values.

Additionally, we need to implement access controls and monitoring mechanisms. By doing so, organizations can effectively protect sensitive data and comply with data protection regulations.

For exceptional tools and solutions for data maintenance and security, including security auditing, masking, and compliance, consider exploring the offerings from DataSunrise. Our team of experts is available for online demos to help you understand how their solutions can benefit your organization. Visit the DataSunrise website to schedule a demo and take the first step towards enhancing your data protection strategy.


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