The Personal Data Equaliser

[Note: This post was originally published on the CiTiP Blog]

The concept of personal data – key in determining data protection law’s material scope of application – may seem pretty straightforward in the abstract. In practice, particularly when assessing the applicability of specific data subject rights, things get a lot murkier.

 Personal Data – a Disharmonious Concept

It is a truism to say that Personal Data constitutes data protection law’s central building block. Indeed, personal data is the key factor in determining the framework’s applicability. Directive 95/46 – as well as the upcoming General Data Protection Regulation (GDPR) – are pretty concise in defining the concept: “any information relating to an identified or identifiable natural person.” No a priori distinction is made between different sources, types, formats, and so on. Only the contentious sub-category of ‘sensitive data’ explicitly enjoys special status. Personal data’s incredibly wide definition has been documented and criticised widely. Data protection law’s ‘information-agnosticism’ is also an important element separating it from the general right to privacy. The latter primarily covering more intimate data (or elements/activities) affecting an individuals’ private sphere.

In reality, not all (personal) data are equal. Many legal texts specify or differentiate particular kinds of personal data because of the heightened risk to the individuals’ rights, interests or freedoms. Even though the data protection framework explicitly refers to some sub-categories of data – sensitive data, (online) identifiers, pseudonyms, traffic and location data – it appears unfeasible to devise an overall taxonomy for personal data. The many attempts that have been made – in privacy policies, by consultants and academics in different fields – fail to offer a satisfactory and comprehensive overview.

The few data categories explicitly/implicitly appearing throughout the GDPR are useful indicators for assessing the extent of rights and obligations. However, the applicability of data subject rights – and the right to erasure in particular – cannot be reduced to a mere qualification of the underlying data in one of these categories. Google’s and Facebook’s privacy policies differentiate personal data on the basis of its origin, its form and/or its function. But data can also be differentiated on the basis of its nature, sensitivity or visibility/obscurity. To complicate things even more, predefined categories often overlap in practice, further rendering nonsensical any attempts at straightjacketing personal data into predefined categories.

Still, the category of data will often impact the exercise of data subject rights. The rights to erasure and to object illustrate this quite well. Different data-types will have a different impact on the data subject and the balancing exercise generally accompanying a request to erase/object. Sensitive data may be the most obvious example of a data category that will generally tip the balance in favour of the data subject. In short, there is clearly some merit in qualifying the relevant personal data in one way or another. In light of the concept’s incredible heterogeneity however, attempts at developing a comprehensive ‘personal data taxonomy’ are doomed from the start.

 Personal Data Equaliser

Instead of trying to come up with a data taxonomy – or even a more modest list of specific data categories – an alternative can be envisaged. From the perspective of exercising one’s data subject rights, it makes more sense to identify relevant variables on a case-by-case basis. These may relate to the data itself (e.g. accuracy, public interest, sensitivity, format), the source (e.g. voluntarily shared, inferred), the data subject (e.g. role in public life, child), time, context, etc. Each of these ‘variables’ – some of which correspond with categories in obsolete data taxonomies – should be seen as non-binary continuums.

By analogy, one could think of an audio equaliser, ubiquitous in eighties’ stereo sound-systems. Every slider represents a variable, impacting – to a greater or lesser extent – what comes out of the speakers. Similarly to its audio-counterpart, the ‘personal data equaliser’, comes with certain pre-sets. For certain situations or ‘data types’, there will be pre-defined defaults. Depending on the circumstances, certain sliders will be hardwired (e.g. format of the data, controller), whereas others might still be tweakable (e.g. visibility/obscurity). Crucially, determining the configuration of parameters is only possible a posteriori, when evaluating the applicability of data subjects’ rights in a particular case.

The Data Equaliser acknowledges the complexity of today’s information processing landscape. It recognises the impossibility of a priori determining the potential implications on an individual of one type of personal data or another. Today’s vast – and quickly expanding – data processing eco-system transforms seemingly trivial and/or anonymous data into personal data and vice versa. Unsurprisingly, determining the reach of data protection rights (notably, the right to erasure) is a tough exercise in the abstract. Though helpful indicators, the personal data categories defined by the legislator do not offer quick-and-easy answers either. The idea behind the ‘personal data equaliser’ recognises the messiness of data and the importance of looking at the particular circumstances of each individual case. It acknowledges the fluidity of ‘personal data’, depending on time and context.

Looking ahead, attempts at bringing more structure to the concept of personal data should focus on identifying potential variables rather than types of personal data. Such a functional approach will be much more valuable to the interpretation of data subject rights in practice.

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