Tech versus virus: Contact tracing
The battle with the coronavirus is dynamically entering another phase. After the initial shock, we are realising that technology may have a crucial impact on the rate of return to a somewhat more normal life. This doesn’t mean just biotech. Solutions keeping the virus under relative control until effective vaccines reach the market can prove just as important.
With this article, we would like to launch a series of publications on the legal aspects of solutions for supporting the battle with the coronavirus. These solutions are extremely interesting from the conceptual and technological perspective, but also entail numerous legal issues.
From a lawyer’s perspective, the first topic that comes to mind is fairly obvious. This has to do with applications based on the concept of contact tracing, monitoring interpersonal interactions to identify individuals infected with the virus. In these solutions, we place great hopes on overcoming the pandemic. But they are based on monitoring human behaviours, which presents fundamental legal challenges.
What’s up with contact tracing?
Contact tracing apps include many potential solutions with essentially the same aim. The point is to effectively identify persons who may be infected with the coronavirus. Identifying these people is one of the key factors for slowing the spread of the virus until society gains herd immunity.
This aim can be achieved through monitoring of human interactions. After determining that a person is carrying the virus, his or her social interactions can then be quickly recreated to reach people whose interaction with the carrier places them in a risk group. These persons might then, for example, be given priority testing for the virus.
This aim can be achieved in many different ways. A range of ideas are being pursued, the situation is dynamic, and it is hard to say at the moment which solutions will prevail. In practice, different solutions may be applied in different regions of the world.
A common feature of many solutions is the technology used to monitor interactions. Most of the apps are based on the Bluetooth function in smartphones. This technology enables automatic sharing of data between devices within a certain distance of each other, allowing smartphones to gather data on the interaction of their users.
A major difference between contact tracing projects is the solutions they adopt for collecting and processing data, and the architecture of the applications, particularly the degree of centralisation or decentralisation of data exchange.
The most centralised models enable collection of data which can unequivocally identify individuals and transmit their data to administrative authorities (such as the sanitary inspectorate, but also the police). At the opposite pole are models providing only for processing of anonymous identifiers and sharing of data on a peer-to-peer model, without involving the public administration.
The differences between these models can be best illustrated by tracing the life cycle of data in the application. Let’s assume that citizens use their smartphones on a mass scale with the Bluetooth function switched on (raising the first serious dilemma: to what degree use of the application should be voluntary or compulsory.) The smartphones record data on their interaction with other smartphones. In practice, certain identifiers generated by the smartphones are recorded (presenting another dilemma on how such identifiers are constructed), along with for example data on the distance and duration of the interactions. These data are recorded on smartphones. Depending on the model selected, the data may also be passed on, e.g. to a central repository of data monitored by administrative authorities. The models can also differ in the length of storage of the collected data. A key moment in the functioning of the app is when it learns that an individual is carrying the virus. Models can differ in how this information reaches the system. Some assume that the citizen will voluntarily submit this information to the system, while others assume top-down introduction of this information by the administration. (In the second variant, clearly the system would have to allow processing of data enabling the administration to compare data on infected persons with data obtained from smartphones.) After information on carriers is entered in the system, the carriers’ interactions with other people could be recreated, either within a central repository or locally on users’ devices. To this end, the carrier’s identifier would need to be compared to the identifiers recorded on the given smartphone. If they match, the app would use the data on distance and duration of interaction to assess the probability of infection of the smartphone’s user. At this moment, another dilemma arises. The information on risk of infection could be accessible only to the smartphone user, or could be automatically transmitted to the authorities.
It should be evident that the number of possible configurations and variants for social tracing applications is large. When the coronavirus reached Europe and the United States, concepts for such apps were examined in light of the approach to privacy and permissible bounds of state intrusion on individual privacy prevailing in those regions. This review has led to several interesting initiatives attempting to develop standards for contact tracing applications complying with the regional peculiarities in approaches to privacy.
One example is the PEPP-PT initiative (Pan-European Privacy-Preserving Proximity Tracing). Its creators are seeking to set certain standards for the operation of proximity-tracing applications. In simple terms, these standards are based on publication of only anonymous identifiers of smartphone users. This model can achieve the aim of notifying an exposed individual that he or she is at risk of infection, without sharing data identifying the carrier. In this context, the concept of temporary contact numbers has also been developed, i.e. randomly generated identifiers that would be replaced at regular intervals. This would increase the anonymity of this solution.
Apple and Google have also engaged in creating applications, joining forces to create a common standard. It includes many elements called for by PEPP-PT. There are many indicators that the Google/Apple standard has a chance to become the dominant standard.
Legal conundrums
Personal data issues obviously pose a fundamental legal challenge for social tracing. There is no need to repeat here the many public comments raised on this issue. Rather, I would like to point out a few less obvious aspects.
As stated, there are a great many potential models for contact tracing applications. This diversity also translates into many possible configurations in the area of personal data. First, in some models it is justified to ask whether the apps would actually be processing personal data at all. Some models are based only on forwarding of anonymous identifiers randomly generated at certain intervals. Some models call for these identifiers to be shared directly only between end users’ devices, using Bluetooth connectivity. Keys would be transmitted to the central server, and only when these are downloaded to the end user’s devices could it be determined whether the specific identifier is stored on a given end user’s device.
Thus we have a whole palette of options. The matter is obvious in the case of apps transmitting data to the central server unequivocally identifying an individual. That will involve processing of personal data. At the opposite end of the spectrum are apps that create several levels of data anonymisation. First, the app is built on anonymised identifiers randomly generated at set intervals. Second, only encryption keys, not the identifiers themselves, would reach the central repository. In the case of such applications, even if we recognise that anonymous identifiers can in certain circumstances enable identification of individuals (in this context I recommend an analysis of the DP3T system), they are processed only at the level of the devices of the end users of the application. In turn, regarding keys as personal data seems at first glance more problematic, particularly if the architecture of the system ensures that only end users can use such keys to establish identifiers.
The second and perhaps even more interesting issue involves the determination of whether certain models actually involve a data controller. This applies particularly to decentralised models, which involve at most transferring to a central server a very narrow bundle of anonymised data. The potential central operator of the repository would process only anonymised identifiers. As a rule, the operator could not associate these identifiers with specific persons. Such association would potentially be possible only at the level of end users. Moreover, some developers are considering placement of anonymous identifiers (or alternatively keys) on a public blockchain, which would even more greatly hinder identification of any data controller. Consequently, it may turn out that even if the apps process information which, under a cautious approach to the definition, could be regarded as personal data, practically there would be no entities in the system obliged to comply with certain duties under the General Data Protection Regulation (such as informational duties with respect to data subjects). The end users, in particular, would not seem to qualify as data controllers, because as a rule they would fall within the exclusion from application of the GDPR for use of the application for purely personal or household activities (Art. 2(2)(c) GDPR).
Another interesting aspect in the context of personal data is potential profiling conducted by these applications. Under the GDPR, “profiling” means any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in this case to analyse or predict aspects concerning the person’s health. This seems to be precisely what social tracing apps would be used for. Art. 22 GDPR contains a crucial provision attempting to create legal protection for individuals whose situation is dependent on automated processing of their personal data, including profiling. Leaving aside the many nuances that would have to be analysed to determine whether Art. 22 would apply at all to processing of data in social tracing apps, I would point to the peculiarity of these apps, which conduct profiling only at the level of end users’ devices. In that case, even if the grounds for applying Art. 22 GDPR were fulfilled, its application would be practically excluded.
In the context of the Polish Telecommunications Law, there are at least two essential elements that must be considered in connection with social tracing applications. First, the operation of the app should be analysed in terms of whether the data processed using the app are covered by telecommunications secrecy. If we identify such data, apart from personal data issues the operating model must be reconciled with the restrictions on processing of telecommunications secrets. Second, the Telecommunications Law also imposes rules for access to data found on an end user’s device and installation of programming on the end user’s device. Apps providing such possibilities must be designed to comply with Art. 173 of the Telecommunications Law.
Just in case, contact tracing apps should also be examined in terms of regulations on medical devices. For some time, along with increasing digitalisation, there has been a growing problem in the legal classification of apps used for monitoring human health. They often fall into a grey area, where it can be very hard to determine whether or not the app constitutes a medical device. This results from the broad definition of medical devices, which extends for example to software intended for use in people for diagnosis, prevention, monitoring, treatment or alleviation of disease. The judgment of the Court of Justice in C-329/16, Snitem, is relevant in this context, along with the European Commission’s guidance document on qualification of standalone software as medical devices.
Admittedly, the foregoing comments are highly abstract. In practice, due to the great differences between particular applications, it is essential to conduct a legal analysis of each model based on the technical details of each solution.
The EU position
EU authorities have also recently issued official positions on contact tracing apps. Particularly notable in this context are the Commission Guidance on Apps supporting the fight against COVID 19 pandemic in relation to data protection and the Common EU Toolbox for Member States on mobile applications to support contact tracing in the EU’s fight against COVID-19.
These documents present a preliminary outline of the architecture of social tracing apps which, taking into account the peculiarities of the European approach to privacy protection, would be desirable from the perspective of EU bodies. Among other things, the guidance calls for appointing public bodies as data controllers, primarily to avoid doubts on which entity is required to carry out duties in the GDPR with respect to data subjects. The suggested basis for processing of personal data is Art. 6(1)(c) and 9(2)(i) GDPR. The point is to ensure transparency in the grounds for processing of data. Data should be processed on end users’ devices and transmitted to a central repository only if there is a positive diagnosis. In line with the principle of data minimisation, the Commission recommends avoiding the processing of location data as unnecessary in the context of the aims of the apps. The guidance also supports decentralised architecture of apps as promoting the principle of data minimisation. There are also specific demands for creation of solutions enabling interoperability between various apps used in different member states.
Setting new boundaries
Notwithstanding the legal nuances, it is important to recognise the broader context of the debate over social tracing apps. In the upcoming weeks, a battle will play out not only over our health, but also over setting new boundaries for protection of privacy and permissible state encroachment on it. Dilemmas that previously could be resolved in processes spread over years, now must be decided in a matter of weeks. Under these circumstances, the desire to bring the pandemic under control may dull our sensitivity to the need to protect our fundamental rights. The risk is that once a barrier has been breached, it may be impossible to rebuild it. In this context, initiatives like the international Data Rights for Exposure Notification or, in Poland, the seven pillars of trust, are vital. They create a chance to ensure that our choices are adequately thought-through, even if they ultimately lead to a redefinition of our notions of privacy.
This article was originally published on the newtech.law blog.
Krzysztof Wojdyło, adwokat, New Technologies practice, Wardyński & Partners
