Anonymization

Humanities research data sometimes contains information related to identifiable persons. Such information is called personal data. Examples include names, addresses, contact details, or affiliations. When personal data has the potential to harm an individual, it is classified as sensitive data. Examples include religious or political views, or gender identity. The distinction between personal and sensitive data is gradual, as potential harm depends on context.

Legal Requirements for Personal Data

DaSCH ensures compliance with legal requirements for personal and sensitive data. Personal or sensitive data can only be stored in DSP under the following conditions:

• When researchers provide Informed Consent Forms (see "Research Ethics"), data can be stored as provided.
• Otherwise, DaSCH supports researchers in anonymizing personal data appropriately.

Anonymization involves identifying and removing information that could lead to re-identification. This includes direct identifiers (names, social security numbers), strong indirect identifiers (home addresses, telephone numbers), and weak indirect identifiers (socio-demographic characteristics that, when combined, could identify someone).

Planning Anonymization Strategy

DaSCH recommends planning an anonymization strategy early in the research project, ideally as part of a Data Management Plan (DMP).

Important: Hiding personal or sensitive data behind access control mechanisms (e.g., restricting visibility to logged-in users) does not meet legal requirements. This approach does not constitute proper anonymization.

Related Resources

• Talk by Prof. Dr. Rita Gautschy and Dr. Auriane Marmier: Data Management Planning (FORS-DaSCH Webinar 1), 06.11.2024, Recording, Slides.
• Talk by Dr. Brian Kleiner: Quantitative Data Anonymisation (FORS data management webinar series), 01.11.2022, Slides.
• SwissAnon, Swiss Competence Centre for Data Anonymisation, http://swissanon.ch/.
• Amnesia, developed by OpenAire, https://amnesia.openaire.eu/.