User Privacy Risk Analysis For The Internet of Things

Akash Aggarwal; Waqar Asif; Habibul Azam; Milan Markovic; Muttukrishnan  Rajarajan; Peter Edwards

doi:10.1109/IOTSMS48152.2019.8939265

User Privacy Risk Analysis For The Internet of Things

Akash Aggarwal, Waqar Asif, Habibul Azam, Milan Markovic, Muttukrishnan Rajarajan, Peter Edwards

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

Abstract

The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem.

Original language	English
Title of host publication	Proceedings of the 6th IEEE International Conference on Internet of Things
Subtitle of host publication	Systems, Management and Security
Publisher	IEEE Explore
Pages	259-264
Number of pages	6
ISBN (Electronic)	978-1-7281-2949-5
ISBN (Print)	978-1-7281-2950-1
DOIs	https://doi.org/10.1109/IOTSMS48152.2019.8939265
Publication status	Published - 1 Feb 2020
Event	The 6th IEEE International Conference on Internet of Things 2019: Systems, Management and Security 2019 - Granada, Spain Duration: 22 Oct 2019 → 25 Oct 2019 Conference number: 6th https://ieeexplore.ieee.org/xpl/conhome/8924808/proceeding

Conference

Conference	The 6th IEEE International Conference on Internet of Things 2019
Abbreviated title	(IOTSMS 2019)
Country/Territory	Spain
City	Granada
Period	22/10/19 → 25/10/19
Internet address	https://ieeexplore.ieee.org/xpl/conhome/8924808/proceeding

Keywords

Privacy
Attack Cost
Attack Impact
Attack Attributes
Attack Scenarios

Access to Document

10.1109/IOTSMS48152.2019.8939265Licence: Unspecified

Cite this

Aggarwal, A, Asif, W, Azam, H, Markovic, M, Rajarajan, M & Edwards, P 2020, User Privacy Risk Analysis For The Internet of Things. in Proceedings of the 6th IEEE International Conference on Internet of Things: Systems, Management and Security . IEEE Explore, pp. 259-264, The 6th IEEE International Conference on Internet of Things 2019, Granada, Spain, 22/10/19. https://doi.org/10.1109/IOTSMS48152.2019.8939265

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title = "User Privacy Risk Analysis For The Internet of Things",

abstract = "The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem.",

keywords = "Privacy, Attack Cost, Attack Impact, Attack Attributes, Attack Scenarios",

author = "Akash Aggarwal and Waqar Asif and Habibul Azam and Milan Markovic and Muttukrishnan Rajarajan and Peter Edwards",

year = "2020",

month = feb,

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doi = "10.1109/IOTSMS48152.2019.8939265",

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booktitle = "Proceedings of the 6th IEEE International Conference on Internet of Things",

publisher = "IEEE Explore",

note = "The 6th IEEE International Conference on Internet of Things 2019 : Systems, Management and Security 2019, (IOTSMS 2019) ; Conference date: 22-10-2019 Through 25-10-2019",

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TY - GEN

T1 - User Privacy Risk Analysis For The Internet of Things

AU - Aggarwal, Akash

AU - Asif, Waqar

AU - Azam, Habibul

AU - Markovic, Milan

AU - Rajarajan, Muttukrishnan

AU - Edwards, Peter

N1 - Conference code: 6th

PY - 2020/2/1

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N2 - The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem.

AB - The Internet of Things (IoT) refers to a large network of devices such as sensors and actuators in which diverse types of data is generated and shared. Data can be shared in its raw form or as a result of data processing activities performed by an IoT device (e.g. anonymization, aggregation, etc.). However, sharing such data introduces a multitude of risks which are influenced by data type, data harvesting granularity, user demographics and the device under consideration. In this work, we propose a novel extension to our attack tree risk model [1] to consider user preferences for sharing personal data. We enrich our earlier work by exploring more attacks and complimenting them with a user privacy-risk model. We evaluate this proposed model and identify a range of scenarios which can result in personal information privacy violation and thus provide a model for estimating the potential risk of an IoT ecosystem.

KW - Privacy

KW - Attack Cost

KW - Attack Impact

KW - Attack Attributes

KW - Attack Scenarios

UR - https://core.ac.uk/download/pdf/356592223.pdf

U2 - 10.1109/IOTSMS48152.2019.8939265

DO - 10.1109/IOTSMS48152.2019.8939265

M3 - Published conference contribution

SN - 978-1-7281-2950-1

SP - 259

EP - 264

BT - Proceedings of the 6th IEEE International Conference on Internet of Things

PB - IEEE Explore

T2 - The 6th IEEE International Conference on Internet of Things 2019

Y2 - 22 October 2019 through 25 October 2019

ER -

User Privacy Risk Analysis For The Internet of Things

Abstract

Conference

Keywords

Access to Document

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Cite this