research-article

Data Privacy Enhancing in the IoT User/Device Behavior Analytics

Authors:

Li Da XuAuthors Info & Claims

ACM Transactions on Sensor Networks, Volume 19, Issue 2

Article No.: 32, Pages 1 - 13

https://doi.org/10.1145/3534648

Published: 20 December 2022 Publication History

Abstract

The Internet of Things (IoT) is generating and processing a huge amount of data that are then used and shared to improve services and applications in various industries. The collected data are always including sensitive information (sensitive data, users/devices/applications behaviors, etc.), which can be exchanged over the IoT to third-party for storing, processing, and sharing with associated applications. It is important to protect data privacy from compromising using consistently privacy preserving techniques. In this work, we propose a privacy-preserving solution for both structured data and unstructured data by using data anonymization techniques, which are able to enhance privacy associated with IoT services, applications, and users/device behavior. This can allow IoT users/devices to access privacy-enhanced data protecting sensitive data against re-identification risks. The experimental results demonstrate that the proposed solution can provide privacy-enhanced data for third-party services and applications over the IoT.

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Cited By

Qian WLi KGuo DHu BWang MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cluster-Phys: Facial Clues Clustering Towards Efficient Remote Physiological MeasurementProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680670(330-339)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680670
Li YZhan XLi HZhang W(2024)Quality assessment of identity inpainting based on multidimensional discriminationMultimedia Systems10.1007/s00530-024-01536-030:6Online publication date: 13-Nov-2024
https://dl.acm.org/doi/10.1007/s00530-024-01536-0
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Index Terms

Data Privacy Enhancing in the IoT User/Device Behavior Analytics
1. Security and privacy
  1. Human and societal aspects of security and privacy

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Published In

cover image ACM Transactions on Sensor Networks

ACM Transactions on Sensor Networks Volume 19, Issue 2

May 2023

599 pages

ISSN:1550-4859

EISSN:1550-4867

DOI:10.1145/3575873

Editor:
Yunhao Liu
Tsinghua University, China

Issue’s Table of Contents

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 ACM 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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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 20 December 2022

Online AM: 25 July 2022

Accepted: 28 April 2022

Revised: 06 March 2022

Received: 30 September 2021

Published in TOSN Volume 19, Issue 2

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Cited By

Qian WLi KGuo DHu BWang MCai JKankanhalli MPrabhakaran BBoll SSubramanian RZheng LSingh VCesar PXie LXu D(2024)Cluster-Phys: Facial Clues Clustering Towards Efficient Remote Physiological MeasurementProceedings of the 32nd ACM International Conference on Multimedia10.1145/3664647.3680670(330-339)Online publication date: 28-Oct-2024
https://dl.acm.org/doi/10.1145/3664647.3680670
Li YZhan XLi HZhang W(2024)Quality assessment of identity inpainting based on multidimensional discriminationMultimedia Systems10.1007/s00530-024-01536-030:6Online publication date: 13-Nov-2024
https://dl.acm.org/doi/10.1007/s00530-024-01536-0
Show More Cited By

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