Differences

This shows you the differences between two versions of the page.

--- dcc:pdpsol:pseudonymization [2026/01/15 12:47] – marlon
+++ dcc:pdpsol:pseudonymization [2026/02/24 12:38] (current) – [Introduction] marlon
@@ Line 1: / Line 1: @@
+{{indexmenu_n>2}}
 ====== Pseudonymization ======
 ===== Introduction =====
-//“Pseudonymization is a de-identification procedure during which personally identifiable information is replaced by a unique alias or code (pseudonym). In some situations, the researcher maintains the link between the unique code and the data subject in a keyfile, while in other projects this connection is not necessary.”//
+Pseudonymization is a de-identification procedure during which personally identifiable information is replaced by a unique alias or code (pseudonym). In some situations, the researcher maintains the link between the unique code and the data subject in a keyfile, while in other projects this connection is not necessary.
 The manner in which pseudonymization is done varies between projects based on the research setup. It is good practice to describe the process of pseudonymization specific to your research either in your data management plan or a separate Pseudonymization Protocol. You can use the examples below to guide you in this process.
@@ Line 9: / Line 10: @@
 This is the simplest form of pseudonymization, where direct identifiers are either removed or replaced by a pseudonymization ID. It can be applied to direct identifiers in a single file or used to connect multiple files.
-First, assign each data subject in your dataset a unique pseudonymization ID. That ID must be unique, non-informative, and non-derivable from personal data. In Excel, you can generate a randomized list of IDs by entering the following formula in the formula bar:  <color #ed1c24>=SORTBY(SEQUENCE(N), RANDARRAY(N))</color>. Here, <color #ed1c24>SEQUENCE(N)</color> generates numbers from 1 to <color #ed1c24>N</color>, and <color #ed1c24>RANDARRAY(N)</color> randomizes their order. Choose an <color #ed1c24>N</color> larger than your sample size (e.g., for 100 data subjects, <color #ed1c24>N</color> = 1000). Remove all direct identifiers (e.g. names, email addresses, phone numbers) or replace them with these IDs. Use the same ID consistently across related files (e.g., transcripts, survey responses, and other data) to preserve linkability while protecting the identity of your data subjects (Figure 2).
+First, assign each data subject in your dataset a unique pseudonymization ID. That ID must be unique, non-informative, and non-derivable from personal data. In Excel, you can generate a randomized list of IDs by entering the following formula in the formula bar:  <color #ed1c24>=SORTBY(SEQUENCE(N), RANDARRAY(N))</color>. Here, <color #ed1c24>SEQUENCE(N)</color> generates numbers from 1 to <color #ed1c24>N</color>, and <color #ed1c24>RANDARRAY(N)</color> randomizes their order. Choose an <color #ed1c24>N</color> larger than your sample size (e.g., for 100 data subjects, <color #ed1c24>N</color> = 1000). Remove all direct identifiers (e.g. names, email addresses, phone numbers) or replace them with these IDs. Use the same ID consistently across related files (e.g., transcripts, survey responses, and other data) to preserve linkability while protecting the identity of your data subjects (Figure 1).
 **Warning:** Pseudonymization does not equal anonymization. Although all direct identifiers have been removed or replaced, it might still be possible to re-identify some data subjects in your data because, in combination, certain attributes (e.g., combination of height, job occupation and location of data collection) may single out an individual.
+**Figure 1: Pseudonymization without a keyfile**
+----
+{{:dcc:pdpsol:pseudonymization:pseudnk.png?direct&400|}}
 ===== Pseudonymization with a keyfile =====
@@ Line 17: / Line 26: @@
 ==== Creating a keyfile ====
-First, create a pseudonymization keyfile (e.g., in Excel or another UG approved tool available in the University Workplace) that assigns each data subject a unique pseudonymization ID. For the creation of the pseudonymization IDs, you can follow the same steps as described in //Pseudonymization without a keyfile//. Direct identifiers such as names, email addresses, or phone numbers should be stored only in this keyfile and removed from the research dataset. In the research dataset, remove all direct identifiers or replace them with the pseudonymization IDs.
+First, create a pseudonymization keyfile (e.g., in Excel or another UG approved tool available in the University Workplace) that assigns each data subject a unique pseudonymization ID. For the creation of the pseudonymization IDs, you can follow the same steps as described in //Pseudonymization without a keyfile//. Direct identifiers such as names, email addresses, or phone numbers should be stored only in this keyfile (Figure 2) and removed from the research dataset. In the research dataset, remove all direct identifiers or replace them with the pseudonymization IDs.
+**Figure 2: Pseudonymization with keyfile**
+----
+{{:dcc:pdpsol:pseudonymization:pseudk.png?direct&400 |}}{{:dcc:pdpsol:pseudonymization:pseudnk.png?direct&400|}}
 ==== Managing the keyfile ====
@@ Line 30: / Line 43: @@
 This approach reduces the risk of re-identification and protects sensitive information throughout the data transfer process. For guidance on implementing this protocol, contact your [[https://www.rug.nl/digital-competence-centre/contact/|faculty data steward or the DCC]].
+**Figure 3: Pseudonymization with double coding**
+----
+{{:dcc:pdpsol:pseudonymization:pseudnk.png?direct&350|}}{{:dcc:pdpsol:pseudonymization:pseuddc.png?direct&350 |}}{{:dcc:pdpsol:pseudonymization:pseuddc2.png?direct&350 |}}
 [[dcc:pdpsol:start | → Go back to the Privacy & Data protection home page]]