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Towards Privacy-Friendly Smart Products

Type
book section
Date Issued
2021-12-13
Author(s)
Garcia, Kimberly  
Zihlmann, Zaïra
Mayer, Simon  orcid-logo
Tamo-Larrieux, Aurelia
Hooss, Johannes
Abstract (De)
Smart products, such as toy robots, must comply with multiple legal requirements of the countries they are sold and used in. Currently, compliance with the legal environment requires manually customizing products for different markets. In this paper, we explore a design approach for smart products that enforces compliance with aspects of the European Union’s data protection principles within a product’s firmware through a toy robot case study. To this end, we present an exchange between computer scientists and legal scholars that identified the relevant data flows, their processing needs, and the implementation decisions that could allow a device to operate while complying with the EU data protection law. By designing a dataminimizing toy robot, we show that the variety, amount, and quality of data that is exposed, processed, and stored outside a user’s premises can be considerably reduced while preserving the device’s functionality. In comparison with a robot designed using a traditional approach, in which 90% of the collected types of information are stored by the data controller or a remote service, our proposed design leads to the mandatory exposure of only 7 out of 15 collected types of information, all of which are legally required by the data controller to demonstrate consent. Moreover, our design is aligned with the Data Privacy Vocabulary, which enables the toy robot to cross geographic borders and seamlessly adjust its data processing activities to the local regulations.
Language
English
HSG Classification
contribution to scientific community
HSG Profile Area
None
Book title
Proceedings of the 18th Annual International Conference on Privacy, Security and Trust
Publisher
IEEE
URL
https://www.alexandria.unisg.ch/handle/20.500.14171/109631
Subject(s)

law

computer science

Division(s)

ICS - Institute of Co...

LS - Law School

Eprints ID
265228
File(s)
Loading...
Thumbnail Image

open.access

Name

paper_27 (1).pdf

Size

150.83 KB

Format

Adobe PDF

Checksum (MD5)

e9586bf34234b3b9317c32030283c724

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