Human Behavior Prediction Though Noninvasive and Privacy-Preserving Internet of Things (IoT) Assisted Monitoring

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Title: Human Behavior Prediction Though Noninvasive and Privacy-Preserving Internet of Things (IoT) Assisted Monitoring
Authors: Xu, LinaPombo, Nuno
Permanent link: http://hdl.handle.net/10197/10935
Date: 18-May-2019
Online since: 2019-07-22T09:38:58Z
Abstract: As the supporting technologies for Ambient Assistant Living (AAL) in Internet of Things (IoT) domain have become more powerful and more attractive, the related systems will be widely deployed and put into action. With all associated embedded IoT sensing devices, how to maintain users’ privacy and data security is a highly concerned problem. There are generally two approaches to protect privacy. One is to implement complex security protocols to guarantee the safety of sensing, storage and data transmission. Another one is to prevent the privacy issues and concerns from the source. This proposed research will provide a concept of a framework that can support behaviour monitoring through noninvasive and privacy preserving sensing. The data collected, transmitted and used for analysing in this framework is sensing information with low richness. This framework aims to increase the users’ perceived privacy in existing monitoring systems to avoid data over-collection and over exposure problems.
Type of material: Conference Publication
Publisher: IEEE
Keywords: Ambient Assistant Living (AAL)Internet of Things (IoT)PrivacyData security
Language: en
Status of Item: Peer reviewed
Conference Details: IEEE 5th World Forum on Internet of Things, Limerick, Ireland, 15-18 April 2019
ISBN: 978-1-5386-4980-0
Appears in Collections:Computer Science Research Collection

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