Personal lifelogging builds upon the pervasive and continuous acquisition of sensor measurements and signals in time, and this may expose the subject, and eventually bystanders, to privacy violations. While the issue is easy to understand for image and video data, the risks associated to the use of wearable accelerometers is less clear and may be underestimated. This work addresses the problem of understanding if acceleration measurements collected from the wrist, by subjects performing different types of Activities of Daily Living (ADLs), may release personal details, for example about their gender or age. A positive outcome would motivate the need for de-identification algorithms to be applied to acceleration signals, embedded into wearable devices, in order to limit the unintentional release of personal details and ensure the necessary privacy by design and by default requirements.

Identification Issues Associated with the Use of Wearable Accelerometers in Lifelogging / Poli, Angelica; Strazza, Annachiara; Cecchi, Stefania; Spinsante, Susanna. - ELETTRONICO. - 12207:(2020), pp. 338-351. (Intervento presentato al convegno International Conference on Human-Computer Interaction HCII 2020 tenutosi a Evento virtuale nel 19-24 Luglio 2020) [10.1007/978-3-030-50252-2_26].

Identification Issues Associated with the Use of Wearable Accelerometers in Lifelogging

Poli, Angelica
Data Curation
;
Strazza, Annachiara
Membro del Collaboration Group
;
Cecchi, Stefania
Writing – Review & Editing
;
Spinsante, Susanna
Writing – Original Draft Preparation
2020-01-01

Abstract

Personal lifelogging builds upon the pervasive and continuous acquisition of sensor measurements and signals in time, and this may expose the subject, and eventually bystanders, to privacy violations. While the issue is easy to understand for image and video data, the risks associated to the use of wearable accelerometers is less clear and may be underestimated. This work addresses the problem of understanding if acceleration measurements collected from the wrist, by subjects performing different types of Activities of Daily Living (ADLs), may release personal details, for example about their gender or age. A positive outcome would motivate the need for de-identification algorithms to be applied to acceleration signals, embedded into wearable devices, in order to limit the unintentional release of personal details and ensure the necessary privacy by design and by default requirements.
2020
Lecture Notes in Computer Science
978-3-030-50251-5
978-3-030-50252-2
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/283280
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