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Privacy-preserving activity and health monitoring on databox

Published: 13 May 2020 Publication History

Abstract

Activity recognition using deep learning and sensor data can help monitor activities and health conditions of people who need assistance in their daily lives. Deep Neural Network (DNN) models to infer the activities require data collected by in-home sensory devices. These data are often sent to a centralised cloud to be used for training the model. Centralising the data introduces privacy risks. The collected data contain sensitive information about the subjects. The cloud-based approach increases the risk that the data be stored and reused for other purposes without the owner's control. We propose a system that uses edge devices to implement activity and health monitoring locally and applies federated learning to facilitate the training process. The devices use the Databox platform to manage sensor data collected in people's homes, conduct activity recognition locally, and collaboratively train a DNN model without transferring the collected data into the cloud. We illustrate the applicability of the processing time of activity recognition on edge devices. We use a hierarchical model in which a global model is generated in the cloud, without requiring the raw data, and local models are trained on edge devices. The activity inference accuracy of the global model converges to a sufficient level after a few rounds of communication between edge devices and the cloud.

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cover image ACM Conferences
EdgeSys '20: Proceedings of the Third ACM International Workshop on Edge Systems, Analytics and Networking
April 2020
78 pages
ISBN:9781450371322
DOI:10.1145/3378679
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 13 May 2020

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Author Tags

  1. activity recognition
  2. edge computing
  3. federated learning

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  • Research-article

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  • UK Dementia Research Institute

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EuroSys '20
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EuroSys '20: Fifteenth EuroSys Conference 2020
April 27, 2020
Heraklion, Greece

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Overall Acceptance Rate 10 of 23 submissions, 43%

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Twentieth European Conference on Computer Systems
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Cited By

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  • (2024)Privacy-preserving human activity sensing: A surveyHigh-Confidence Computing10.1016/j.hcc.2024.1002044:1(100204)Online publication date: Mar-2024
  • (2024)DCLA: Towards Distributed Cooperative Learning Analytics for Developing CommunitiesHCI International 2024 – Late Breaking Papers10.1007/978-3-031-76815-6_8(94-106)Online publication date: 11-Dec-2024
  • (2023)Applications of Federated Learning in Mobile Health: Scoping ReviewJournal of Medical Internet Research10.2196/4300625(e43006)Online publication date: 1-May-2023
  • (2023)Personalized Federated Human Activity Recognition through Semi-supervised Learning and Enhanced RepresentationAdjunct Proceedings of the 2023 ACM International Joint Conference on Pervasive and Ubiquitous Computing & the 2023 ACM International Symposium on Wearable Computing10.1145/3594739.3610739(463-468)Online publication date: 8-Oct-2023
  • (2023)A Privacy and Efficiency-Oriented Data Sharing Mechanism for IoTsIEEE Transactions on Big Data10.1109/TBDATA.2022.31481819:1(174-185)Online publication date: 1-Feb-2023
  • (2023)PRISM: Privacy Preserving Healthcare Internet of Things Security Management2023 IEEE Symposium on Computers and Communications (ISCC)10.1109/ISCC58397.2023.10218268(1-5)Online publication date: 9-Jul-2023
  • (2023)Edge AI Empowered Personalized Privacy-Preserving Glucose Prediction with Federated Deep Learning2023 IEEE International Conference on E-health Networking, Application & Services (Healthcom)10.1109/Healthcom56612.2023.10472368(224-230)Online publication date: 15-Dec-2023
  • (2023)Resource optimizing federated learning for use with IoT: A systematic reviewJournal of Parallel and Distributed Computing10.1016/j.jpdc.2023.01.006175(92-108)Online publication date: May-2023
  • (2022)Ethical considerations in design and implementation of home-based smart care for dementiaNursing Ethics10.1177/0969733021106298029:4(1035-1046)Online publication date: 1-Feb-2022
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