What is Data Pseudonymization?

This is essential to understand as it determines whether the GDPR would be applicable or not. The advantages of pseudonymisation include improved privacy, increased data sharing, and better security, whereas its disadvantages include a risk of re-identification, reduced data utility, and complexity. Understanding the differences between these two techniques is crucial to safeguard individuals’ personal data as confusing them could lead to unnecessary restrictions on data use or retention, impacting its value for research or other purposes. The effectiveness (and legality) of both anonymization and pseudonymization hinge on their abilities to protect data subjects from re-identification.

What the GDPR says about pseudonymization – and you should know

Finally, system availability is important because a Denial-of-Service attack can, for example, be used to hide other attacks from users and system administrators. The collection of data and biospecimens which characterize patients and probands in-depth is a core element of modern biomedical research. Relevant data must be considered highly sensitive and it needs to be protected from unauthorized use and re-identification. Instead, they give you a plastic keycard with a random-looking number on it. To the hotel’s computer system, that number is linked to you, “Jane Doe in Room 402.” But to anyone else who finds that keycard on the floor, it’s just a piece of plastic with a meaningless number.

Use automation to remove human error and streamline data protection

Pseudonymization is a technique that is used to reduce the chance that personal data records and identifiers lead to the identification of the natural person (data subject) whom they belong too. With pseudonymization, it’s easier to share sensitive data across organizations, departments, or third parties. This technique not only allows collaboration but also helps companies comply with data privacy regulations, without comprising individual privacy. However, please note that where UCL shares pseudonymised data with a third party acting as a data processor for UCL (where it cannot process data for its own purposes) – that data should still be considered pseudonymised personal data within the scope of the UK GDPR.

Automation tools detect the existence of sensitive information in corporate documents and databases. They automatically apply algorithms to hide personal data without manual input from data protection officers. Automation ensures that all relevant data passes through security filters. Automation also operates consistently, eliminating data entry or formatting errors. Pseudonymization can be reversed by providing extra information, while anonymization cannot. This means that pseudonymized data counts as personal data under GDPR, while fully anonymized data does not.

Products and services

Pseudonymization is a pragmatic privacy technique balancing data utility and identity protection. It requires careful architecture, mature operational practices, and strong key and access controls. When implemented correctly, it reduces risk, enables compliant data use, and preserves analytics capabilities. For example, instead of storing credit card numbers directly, a system can use tokenization to generate unique tokens for each credit card number. These tokens are stored alongside the original data, but the actual credit card numbers are kept in a secure, separate location, only accessible through a key. This way, if a data breach occurs, the exposed tokenized data is useless without access to the key.

Minimized risk of data breaches

• This article should therefore serve as a cautionary tale of the benefits and limitations of early adoption of de-identification techniques as a central aspect to privacy compliance.
• It works best with other techniques like pseudonymization, layered access restrictions, and ongoing risk assessments.
• Only under strict contracts, auditability, and with controlled access to mapping or re-id services.
• In some cases, it may not be possible to fully anonymize data while still retaining its usefulness for analysis or research purposes.

Anonymization is a permanent change where data can’t be traced back to an individual, making it no longer personal data under GDPR. Pseudonymization, however, disguises the data but allows for re-identification with the right additional information. For anonymization, we remove or aggregate any information that could potentially identify the patients. This might include altering diagnosis dates or removing specific conditions if they are rare enough to identify individuals. Here, “Patient001,” “Patient002,” and “Patient003” replace the real names. If necessary, a secure and separate key can match these IDs back to the actual patients.

Detail the risks

• The documentation available on the project homepage is outdated and only covers the first version of the software in German 34.
• You are responsible for deciding whether to implement pseudonymisation and how to do so.
• Understanding these differences will help you choose the best solution for your needs.
• This increases complexity in terms of the number of interfaces that need to be implemented for communicating between the various subsystems 20, 23.

Tokenization enables customer service representatives, for example, to have just enough https://rogerdmoore.ca/ai-main/ai-for-cybersecurity information to assist a customer and otherwise see only artificial identifiers obscuring most other details. This approach comes in handy when only parts of data need to be protected, typically for lines of business where some employees can have complete or mostly complete access to data, while many others have only limited access. For instance, the doctors and nurses of a medical office need full access to a patient’s health records but usually not billing data, whereas the business staff needs to see only the latter. Hold on to data too tightly—by restricting access and inevitably slowing projects such as AI-supported analytics—and you’ve already lost the potential insights it can deliver. Protect data minimally, and you run the risk of violating privacy laws and losing the business of customers and clients who expect their data to be safeguarded.

Values will map to a new space (e.g., value X will map to Z, instead of Y). Not only will this increase the cardinality of values, but make terms aggregations and visualizations, as well as most machine learning jobs ineffective – see “Other Challenges of Pseudonymization”. Users should consider choosing keys based on the relative importance of data correlation and usefulness of data versus GDPR compliance and risk assessment. Pseudonymising a new field now simply requires updating the “fields” parameter of the “script_params”. This snippet in no way represents a final solution – the script is missing tests for a start, doesn’t handle complex data structures and only supports SHA256! Rather it highlights one possible approach and provides a starting point.

Several types of data should be considered candidates for pseudonymization. We’ll refer to the fields that contain such data as “identifier fields.” Similarly, a research hospital can anonymize sensitive personal data but keep intact health data when running AI-driven analytics to study preventative medicine or experimental treatments. The process is reversible, allowing authorized users to view and manage the protected data afterwards. Pseudonymization hides elements of data by replacing information fields with artificial identifiers, or pseudonyms.

Several articles and studies have previously addressed the topic of pseudonymization and its role in medical research. Kohlmayer et al. 10 explored the challenges of pseudonymization in the context of real-world data collection, emphasizing the importance of balancing data protection with research needs. Similarly, Lautenschläger et al. 12 provided a solution for the web-based management of pseudonymized data, focusing on scalability and security in distributed research environments. The European Union Agency for Cybersecurity (ENISA) outlines different pseudonymization scenarios and provides detailed technical recommendations on methods and best practices in its report 8. However, it does not offer a comparison or even a recommendation of specific pseudonymization tools.

Most importantly, automating the pseudonymization process saves time, allowing teams to focus on core tasks and boosting overall productivity, which contributes to long-term success. Pseudonyms can replace real user identifiers to better analyze system performance, track user reputation, protect identities during security investigations, and more. A new guideline from European data protection experts shows exactly how this can be implemented. This way, the data remains useful for health analysis and other operations while ensuring data privacy and mitigating the risk of data theft or misuse. This technique is applicable in scenarios requiring realistic data, such as application development and testing environments, data warehousing, analytical data stores, training programs, and other business processes.

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