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Protecting Mobile Food Diaries from Getting too Personal

Authors:

Lakmal Meegahapola,

Salvador Ruiz-Correa,

Daniel Gatica-PerezAuthors Info & Claims

MUM '20: Proceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia

Pages 212 - 222

https://doi.org/10.1145/3428361.3428468

Published: 22 November 2020 Publication History

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.

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Cited By

Meegahapola LHassoune HGatica-Perez D(2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659591
Mäder AMeegahapola LGatica-Perez D(2024)Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642444(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642444
Nanchen AMeegahapola LDroz WGatica-Perez D(2023)Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity ScoresProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604688(217-228)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604688
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MUM '20: Proceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia

November 2020

353 pages

ISBN:9781450388702

DOI:10.1145/3428361

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MUM 2020: 19th International Conference on Mobile and Ubiquitous Multimedia

November 22 - 25, 2020

Essen, Germany

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Meegahapola LHassoune HGatica-Perez D(2024)M3BAT: Unsupervised Domain Adaptation for Multimodal Mobile Sensing with Multi-Branch Adversarial TrainingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/36595918:2(1-30)Online publication date: 15-May-2024
https://dl.acm.org/doi/10.1145/3659591
Mäder AMeegahapola LGatica-Perez D(2024)Learning About Social Context From Smartphone Data: Generalization Across Countries and Daily Life MomentsProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642444(1-18)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642444
Nanchen AMeegahapola LDroz WGatica-Perez D(2023)Keep Sensors in Check: Disentangling Country-Level Generalization Issues in Mobile Sensor-Based Models with Diversity ScoresProceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society10.1145/3600211.3604688(217-228)Online publication date: 8-Aug-2023
https://dl.acm.org/doi/10.1145/3600211.3604688
Kammoun NMeegahapola LGatica-Perez D(2023)Understanding the Social Context of Eating with Multimodal Smartphone Sensing: The Role of Country DiversityProceedings of the 25th International Conference on Multimodal Interaction10.1145/3577190.3614129(604-612)Online publication date: 9-Oct-2023
https://dl.acm.org/doi/10.1145/3577190.3614129
Assi KMeegahapola LDroz WKun PDe Götzen ABidoglia MStares SGaskell GChagnaa AGanbold AZundui TCaprini CMiorandi DZarza JHume ACernuzzi LBison IRodas Britez MBusso MChenu-Abente RGiunchiglia FGatica-Perez D(2023)Complex Daily Activities, Country-Level Diversity, and Smartphone Sensing: A Study in Denmark, Italy, Mongolia, Paraguay, and UKProceedings of the 2023 CHI Conference on Human Factors in Computing Systems10.1145/3544548.3581190(1-23)Online publication date: 19-Apr-2023
https://dl.acm.org/doi/10.1145/3544548.3581190
Bangamuarachchi WChamantha AMeegahapola LRuiz-Correa SPerera IGatica-Perez D(2022)Sensing Eating Events in Context: A Smartphone-Only ApproachIEEE Access10.1109/ACCESS.2022.317970210(61249-61264)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3179702
Meegahapola LLabhart FPhan TGatica-Perez D(2021)Examining the Social Context of Alcohol Drinking in Young Adults with Smartphone SensingProceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies10.1145/34781265:3(1-26)Online publication date: 14-Sep-2021
https://dl.acm.org/doi/10.1145/3478126
Meegahapola LGatica-Perez D(2021)Smartphone Sensing for the Well-Being of Young Adults: A ReviewIEEE Access10.1109/ACCESS.2020.30459359(3374-3399)Online publication date: 2021
https://doi.org/10.1109/ACCESS.2020.3045935
Meegahapola LRuiz-Correa SGatica-Perez D(2020)Alone or With Others? Understanding Eating Episodes of College Students with Mobile SensingProceedings of the 19th International Conference on Mobile and Ubiquitous Multimedia10.1145/3428361.3428463(162-166)Online publication date: 22-Nov-2020
https://dl.acm.org/doi/10.1145/3428361.3428463

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