Go Privacy Go: Lessons Learned for Data Protection by Design and Default from Designing a Privacy-Friendly GoPiGo Toy Robot

Item Type Conference or Workshop Item (Speech)
Abstract

As smart products move between jurisdictions, their program code becomes subject to various and sometimes incompatible legal environments. Manufacturers are therefore required to create customized product variants for specific markets, which induces variance management overhead and undermines economies of scale. In our article we investigate how the legal environment of a smart product interacts with the programming of that product. Specifically, we are interested in how the General Data Protection Regulation (GDPR) principles can be mapped to legally relevant aspects of toy robots. These are of particular interest as they contain different kinds of privacy-sensitive sensors such as microphones and cameras, are continuously processing (personal) data, can easily be moved from one jurisdiction to another, and affect individuals, including vulnerable ones such as children, in their homes.
The core goal of this article is to develop a methodology to map the GDPR’s principles to the program code of a GoPiGo3 toy robot. We describe this methodology and demonstrate a concrete mapping to GoPiGo3 (as a prototype). In this prototype, the robot’s functionality has been extended to include external face recognition services, as well as external data processing for direct advertising purposes, in order to apply within the research domain of privacy and especially privacy by design. In this article, we describe how the mapping can be done in principle and plan to make first steps towards automating the mapping process. The main research questions we analyze are: How can we describe data protection law’s core principles in a way that system and software engineers can implement such norms into device firmware? What difficulties arise and what implementation decisions have to be taken in order to enable encoding data protection principles into systems? What are the benefits and limits of our methodology to map the data protection principles into a device’s program code, specifically regarding the automation potential of this process?
To answer our research questions, we start by sketching the data flow emanating from GoPiGo3 and the fictional, yet realistic, additional services within our application scenario. We then investigate upon what “lawful grounds” the data processing of the device takes place (Art. 5(1)(a) GDPR) to determine what consent - and by whom depending on the legislation of EU member states on children consent - must be given and which other legal grounds for processing can justify the processing (Art. 6 GDPR). The GoPiGo3 provides information and obtains consent from the user in accordance with Art. 13 of the GDPR given the robot and user context (e.g., location and applicable jurisdiction, user age, etc.). We dive into (legally) contested terminologies, such as the term ‘fairness’, and determine their mapping into GoPiGo3’s program code. We then determine which data items are collected by the software and for which purposes that data is actually processed in order to determine which data items are required and which ones are not. Upon this basis we discuss how the principles of purpose limitation, data minimization, and storage restrictions should be implemented in device code.

Authors Tamò-Larrieux, Aurelia; Mayer, Simon; Zihlmann, Zaïra & Hooss, Johannes
Journal or Publication Title BILETA 2020 Conference on Regulating Transitions in Technology and Law
Language English
Subjects computer science
law
HSG Classification contribution to scientific community
Date May 2020
Title of Book BILETA 2020 Conference on Regulating Transitions in Technology and Law
Event Title BILETA 2020 Conference on Regulating Transitions in Technology and Law
Depositing User Prof. Dr. Simon Mayer
Date Deposited 06 May 2020 19:03
Last Modified 11 May 2020 07:34
URI: https://www.alexandria.unisg.ch/publications/260196

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Tamò-Larrieux, Aurelia; Mayer, Simon; Zihlmann, Zaïra & Hooss, Johannes: Go Privacy Go: Lessons Learned for Data Protection by Design and Default from Designing a Privacy-Friendly GoPiGo Toy Robot. [Conference or Workshop Item]

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https://www.alexandria.unisg.ch/id/eprint/260196
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