Privacy protection

Consent is the process by which a researcher discloses appropriate information about the research, so that a participant can choose whether or not to participate. It is an important instrument to comply with legal obligations and to pass the ethical test. Consent must be 'informed' and also 'freely given' (voluntarily), as stipulated in Article 4(11) of the GDPR.

On the one hand, the researcher takes into account his/her obligations to protect the anonymity and confidentiality of his/her participants by requesting permission; on the other hand, it is a legal means to ask participants in advance whether the data may be published and reused by others.

For sure! For example, take a look at:

• A template for informed consent - in line with the GDPR - developed by Delft University of Technology (2022).

In the GDPR, only the term 'Consent' exists; the term 'Informed Consent' does not occur. This term originates from seeveral key human rights legislations (De Sutter et al., 2022).

Article 4, paragraph 11 of the GDPR says the following about consent:

Consent of the data subject means any freely given, specific, informed and unambiguous indication of the data subject's wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her. 

Consent contains several aspects:

• Freely given;
• Specific;
• Informed;
• Unambiguous indication of the data subject's wishes by which he or she, by a statement or by a clear affirmative action, signifies agreement to the processing of personal data relating to him or her.

One of these four aspects is 'informed'. In this sense, one can speak of 'informed consent' as a condition for the purpose: legally valid consent. But the goal is not 'informed consent' in itself, as is the case with the Clinical Trial Regulations. 

• Have a look at the Guide 'Informed consent for data sharing' by Utrecht University (n.d.).
• Have a look at the CESSDA Data Management Expert Guide (2017a) for tips. 

Pseudonymisation and anonymisation are two different terms that fall under different categories in the General Data Protection Regulation (GDPR, European Union, 2016). While anonymisation aims at irreversibly preventing any way of identifying the data subject, pseudonymisation makes it theoretically possible to re-identify the data subject with additional information. The data are encrypted upon pseudonymisation, but in principle they can still be traced back to the original identity of the research participant. So, pseudonymisation does not mean anonymisation, but is a method for reducing privacy risks.

The LCRDM offers some guidance on pseudonymisation and anonymisation.

That depends on your definition of anonymity. 

The GDPR says that appropriate technical measures must be taken to protect the data and thus the rights of European citizens (European Union, 2016). In addition, these measures should also be applied in accordance with the 'current state of the art'. But what exactly is appropriate? And if data is secured according to the state of the art of today, is that sufficient for tomorrow's data? Couldn't today's anonymous data turn out to be 'not anonymous anymore' tomorrow? These are all questions to which the answers are not yet concrete. In order to be able to work with anonymisation as a tool, institutions work with the concept that they have demonstrably done their best to anonymise data. In this way, anonymisation becomes a legal concept. 

Risk-based anonymisation is a useful tool in the search for a balance between protecting the individual and optimising the data potential. Within the LCRDM, a small group is working on the concept of risk-based protection in a number of research scenarios: common situations for which you could prescribe standard measures. 

A number of entrances with tips and tools:  

• OpenAire offers the Amnesia tool (OpenAIRE, n.d.) which promises to remove identifying information from data and - according to the current state of technology - to make it truly anonymous. A case study about the use of Amnesia is available on the site of the EOSC Hub (n.d.). 
• The CESSDA Data Management Expert Guide offers a chapter with extensive tips for the anonymisation of research data from quantitative and qualitative social scientific research (CESSDA, 2017c).   
• For inspiration, take a look at the infographic 'A visual guide to practical de-identification' of the Future of Privacy Forum (2017). 

RDNL asked Marlon Domingus, Data Protection Officer at Erasmus University Rotterdam (EUR), to explain the GDPR in general and the role of the data supporter in answering privacy related questions in particular. You can find the interview in the form of a Q&A in the next paragraph. 

Since the introduction of the GDPR in May 2018, institutions have been busy arranging the necessary support. How do employees know what to do? What does the GDPR mean in practice? At the Vrije Universiteit Amsterdam they are experimenting with the concept of privacy champions. RDNL interviewed the brains behind the idea.

Privacy designer is a self-assessment tool that allows you to identify the biggest privacy risks and then work on choosing the most appropriate privacy by design strategies (SURF, n.d.a.): 

• Collect less;
• Split;
• Make it abstract;
• Hide;
• Save less;
• Inform;
• Apply data subjects' rights;
• Enforce;
• Show.

Each strategy is explained on the basis of practical examples. 

• You may have to deal with personal data outside interview projects. For example, the personal data from real life event logs (van Dongen, 2011) from Eindhoven first had to be cleaned before they could be included in open access in 4TU.Centre for Research Data.
• The article 'Realities of data sharing using the genome wars as case study - an historical perspective and commentary' provides an historical overview of the dilemmas and forces that played a role in the human genome project: how did privacy and open access relate to each other? (Jasney, 2013).
• A case about the use of Amnesia is available on the site of EOSC-Hub (n.d.).