Guideline 04/2020 – Use of location data and contact tracing tools in the context of COVID-19 outbreak
Section 2.2 Focus on the use of anonymised location data
14 The EDPB emphasises that when it comes to using location data, preference should always be given to the processing of anonymised data rather than personal data.
15 Anonymisation refers to the use of a set of techniques in order to remove the ability to link the data with an identified or identifiable natural person against any “reasonable” effort. This “reasonability test” must take into account both objective aspects (time, technical means) and contextual elements that may vary case by case (rarity of a phenomenon including population density, nature and volume of data). If the data fails to pass this test, then it has not been anonymised and therefore remains in the scope of the GDPR.
16 Evaluating the robustness of anonymisation relies on three criteria: (i) singling-out (isolating an individual in a larger group based on the data); (ii) linkability (linking together two records concerning the same individual); and (iii) inference (deducing, with significant probability,unknown information about an individual).
17 The concept of anonymisation is prone to being misunderstood and is often mistaken for pseudonymisation. While anonymisation allows using the data without any restriction, pseudonymised data are still in the scope of the GDPR.
18 Many options for effective anonymisation exist, but with a caveat. Data cannot be anonymised on their own, meaning that only datasets as a whole may or may not be made anonymous. In this sense, any intervention on a single data pattern (by means of encryption, or any other mathematical transformations) can at best be considered a pseudonymisation.
19 Anonymisation processes and re-identification attacks are active fields of research. It is crucial for any controller implementing anonymisation solutions to monitor recent developments in this field, especially concerning location data (originating from telecom operators and/or information society services) which are known to be notoriously difficult to anonymise.
20 Indeed, a large body of research has shown that location data thought to be anonymised may in fact not be. Mobility traces of individuals are inherently highly correlated and unique. Therefore, they can be vulnerable to re-identification attempts under certain circumstances.
21 A single data pattern tracing the location of an individual over a significant period of time cannot be fully anonymised. This assessment may still hold true if the precision of the recorded geographical coordinates is not sufficiently lowered, or if details of the track are removed and even if only the location of places where the data subject stays for substantial amounts of time are retained. This also holds for location data that is poorly aggregated.
22 To achieve anonymisation, location data must be carefully processed in order to meet the reasonability test. In this sense, such a processing includes considering location datasets as a whole, as well as processing data from a reasonably large set of individuals using available robust anonymisation techniques, provided that they are adequately and effectively implemented.
23 Lastly, given the complexity of anonymisation processes, transparency regarding the anonymisation methodology is highly encouraged.