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 [BibTeX] [Marc21]
Protecting Mobile Food Diaries from Getting too Personal
Type of publication: Conference paper
Citation: Meegahapola_MUM_2020
Publication status: Published
Booktitle: 19th International Conference on Mobile and Ubiquitous Multimedia
Series: MUM 2020
Year: 2020
Month: November
Pages: 212–222
Publisher: Association for Computing Machinery
Location: Essen, Germany
Address: New York, NY, USA
ISBN: 9781450388702
URL: https://doi.org/10.1145/342836...
DOI: 10.1145/3428361.3428468
Abstract: Smartphone applications that use passive sensing to support human health and well-being primarily rely on: (a) generating low-dimensional representations from high-dimensional data streams; (b) making inferences regarding user behavior; and (c) using those inferences to benefit application users. Meanwhile, sometimes these datasets are shared with third parties as well. Human-centered ubiquitous systems need to ensure that sensitive attributes of users are protected when applications provide utility to people based on such behavioral inferences. In this paper, we demonstrate that inferences of sensitive attributes of users (gender, body mass index category) are possible using low-dimensional and sparse data coming from mobile food diaries (a combination of sensor data and self-reports). After exposing this potential risk, we demonstrate how deep learning techniques can be used for feature transformation to preserve sensitive user information while achieving high accuracies for application-related inferences (e.g. inferring the type of consumed food). Our work is based on two datasets of daily eating behavior of 160 young adults from Switzerland (NCH=122) and Mexico (NMX=38). Results show that using the proposed approach, accuracies in the order of 75%-90% can be achieved for application related inferences, while reducing the sensitive inference to almost random performance.
Projects Idiap
Authors Meegahapola, Lakmal Buddika
Ruiz-Correa, Salvador
Gatica-Perez, Daniel
Added by: [UNK]
Total mark: 0
  • Meegahapola_MUM_2020.pdf