Healthcare organizations often align Cerner environments with frameworks such as SOC 2, ISO/IEC 27001, HITRUST CSF, and applicable state or international privacy obligations. Core capabilities include Data discovery and classification across endpoints, SharePoint, Exchange, and databases, Traffic monitoring and control across 100+ services, File lifecycle tracking and visibility.. This integrates tokenization directly into Policy-as-Code frameworks, ensuring purpose limitation is enforced at the moment of data re-identification. In a tokenization architecture, the PEP sits in front of the token vault, validating that the requesting service, user, and context have legitimate purpose before detokenization occurs. A technical comparison of three distinct data obfuscation methods used to enforce purpose limitation and protect sensitive information in AI training pipelines. The core architectural component of tokenization is a secure, isolated database called a token vault that stores the deterministic mapping between the original sensitive value and its surrogate token.

From the remaining 92 articles, we extracted 20 pseudonymization tools. A formal quality assessment of the selected papers was not performed, as the primary use of the papers was simply to identify pseudonymization tools. Flow diagram of the selection process for pseudonymization tools (based on ) The https://www.faststartfinance.org/kv-berlin-muster-datenschutz/ specific search queries for both databases are provided in the Supplementary File 1. Our search string combined the context of pseudonymization with our aim to identify software tools or services.

Anonymization and pseudonymization are both essential data protection strategies under GDPR that can assist companies in safeguarding personal data, wherever possible. In some cases, it may not be possible to https://higgertylaw.ca/blog/what-ethical-guidelines-govern-lawyers-use-of-generative-ai fully anonymize data while still retaining its usefulness for analysis or research purposes. Unlike pseudonymization, anonymized data cannot be used for any purpose that requires identifying information.

Regularly maintain tables to remove data from historical files​

Pseudonymization is a critical data protection technique that replaces direct identifiers in DICOM headers with artificial pseudonyms, enabling longitudinal data linkage while preserving patient privacy. The process is critical for clinical trials and longitudinal research where patient-specific data linkage must be preserved across disparate DICOM data sets. The core mechanism relies on a secure mapping table or a one-way hashing function with a secret salt to generate the pseudonym. Unlike full anonymization, this method maintains referential integrity, allowing researchers to correlate multiple studies from the same patient over time without accessing their true identity. DICOM pseudonymization is the process of replacing identifying data elements in a DICOM object with artificial identifiers, or pseudonyms, to protect patient privacy while preserving the ability to link data longitudinally.

For short-term studies and smaller local projects, the (3) OpenPseudonymiser and the (4) OPT can be recommended, as they support the most features, including pseudonym spaces, record linkage and secondary pseudonymization. Our review identified and we systematically analyzed ten pseudonymization tools for biomedical research and highlighted seven tools that demonstrate particular strengths in addressing key requirements of medical research projects. Suitability of pseudonymization tools for projects with different properties Figure 3 illustrates the suitability of pseudonymization tools for projects with different properties. Moreover, the ability to link and update the data managed by the service is likely to become critical. The remainder of this comparison focuses on the largest group of tools, i.e., those designed to support the pseudonymization of existing as well as newly collected data.

While both protect data, tokenization and encryption operate on fundamentally different principles with distinct security and operational implications. This scope minimization is a primary driver of tokenization adoption in payment processing and healthcare data architectures. This property fundamentally differentiates tokenization from encryption, where a compromised key can expose all protected data. This characteristic eliminates the need to refactor application logic when integrating tokenization into existing payment or data processing workflows. Tokens are engineered to match the data type and length of the original sensitive value, ensuring that downstream applications and databases function without schema modifications. Tokenization is a non-algorithmic substitution method that replaces sensitive data elements with non-sensitive equivalents, preserving format and utility while eliminating the exposure of the original value in downstream systems.

Tokenization preserves format and data type, allowing applications to function without modification while the sensitive values remain isolated. Unlike encryption, tokens are not mathematically reversible without access to the vault, making them ideal for protecting structured data fields like credit card numbers or social security numbers in payment and CRM systems. How do you preserve referential integrity when masking relational databases? What is the difference between data masking, anonymization, and pseudonymization? Why is data masking critical for AI and machine learning development? A non-algorithmic substitution method where a sensitive data element is replaced with a randomly generated token that has no mathematical relationship to the original value.

• The original sensitive values are permanently replaced in the target environment.
• The UK NCSC has published a migration timeline that expects organizations to complete discovery and assessment by 2028, complete highest-priority migration activities by 2031, and complete migration to post-quantum cryptography by 2035.
• DICOM Pseudonymization engines must detect these regions using pattern recognition and apply destructive overlay masks.
• Companies must evaluate their data use cases, compliance needs, and security requirements before choosing a method.

Is pseudonymised data still personal data?

• A formal quality assessment of the selected papers was not performed, as the primary use of the papers was simply to identify pseudonymization tools.
• This guide covers the current state of PIPA as of 2026, including the landmark amendments that have reshaped enforcement and penalties, and the record fines that signal a new era of regulatory seriousness.
• This is often the case in situations where data is being shared with third parties, such as in data breaches, where the data must be completely anonymous to prevent further harm to the data subjects.
• The EMR warehouse included information such as year of birth, sex, marital status, number of children, socio-professional category, visit dates, diagnoses, symptoms, allergies, weight, height, pulse, prescriptions, vaccines, exams, and work stoppages.

Some common examples of sensitive information include postal code, location of individuals, names of individuals, race and gender, etc. Before the Schrems II ruling, pseudonymization was a technique used by security experts or government officials to hide personally identifiable information to maintain data structure and privacy of information. Less than two weeks later, the EU Commission highlighted pseudonymization as an essential element of the equivalency decision for South Korea, which is the status that was lost by the United States under the Schrems II ruling by the Court of Justice of the European Union (CJEU). The European Data Protection Supervisor (EDPS) on 9 December 2021 highlighted pseudonymization as the top technical supplementary measure for Schrems II compliance. A single pseudonym for each replaced field or collection of replaced fields makes the data record less identifiable while remaining suitable for data analysis and data processing. Documented RTO/RPO targets and routine DR testing ensure your recovery plan works when it matters.

There are various methodologies, guidelines and standards for securing information systems, also in the biomedical domain . We note that our selection is not a representative sample of papers about studies employing pseudonymization, but a selection of papers presenting concrete systems while emphasizing security and privacy aspects. Some concepts even introduce additional services that perform further pseudonymization steps (e.g. mapping first-tier pseudonyms to second-tier pseudonyms) and implement hardware-level protection for this service using Smart Cards 14, 15. A schematic overview of the basic attack scenario addressed by research data pseudonymization is shown in Fig. The data stored in the various databases is typically linked to each other using random alphanumerical identifiers (pseudonyms) but further approaches, e.g. using cryptographic schemes, have also been proposed. As a primary data protection mechanism, laws, regulations, guidelines and best-practices often recommend or mandate pseudonymization.

The search covered PubMed and Web of Science to identify pseudonymization tools documented in the scientific literature. Due to its importance, a wide range of pseudonymization tools and services have been developed, and researchers face the challenge of selecting an appropriate tool for their research projects. This section collects any data citations, data availability statements, or supplementary materials included in this article. The supplementary materials include a description of our literature search process. In this work, we focused on the implementation of pseudonymization into data collecting processes. The results are consistent with observations by Neubauer et al. that current pseudonymization architectures are based upon an implicit threat model which has not yet been formalized and by Deng et al. that it needs to be clarified which entities are to be protected from which threats .