Automated approach to analyze IoT privacy policies

Subahi, Alanoud ORCID: https://orcid.org/0000-0002-8642-1708 and Theodorakopoulos, Georgios ORCID: https://orcid.org/0000-0003-2701-7809 2022. Automated approach to analyze IoT privacy policies. Cagáňová, Dagmar and Horňáková, Natalia, eds. Industry 4.0 Challenges in Smart Cities, Cham: Springer, pp. 163-186. (10.1007/978-3-030-92968-8_12)

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Abstract

The massive popularity of IoT devices raises new challenges for user privacy. Hence, manufacturers are obliged to notify users about their privacy practices as well as give them choices to have control over their data. Privacy policies are long and full of legal jargon, thus not understandable by average users. The problem becomes worse with IoT devices due to the ability of these devices to access sensitive information about users. Previous research has addressed problems related to websites and mobile privacy policies. However, few works focus on analyzing IoT privacy policies. In this chapter, we analyze and annotate 50 IoT privacy policies to determine whether the IoT manufacturers collect personal information about the user as well as the type of such information. To ensure that we extract the correct information, we study in depth the complicated and ambiguous sentences that average users will not understand. With our method, we aim to mimic how an ordinary person reads and understands such policies sentence by sentence. We use supervised machine learning to label the collected personal information according to its sensitivity level to either sensitive personal information or non-sensitive personal information. The high accuracy achieved by the classifier (98.8%) proves its validity and reliability.

Item Type:	Book Section
Date Type:	Publication
Status:	Published
Schools:	Computer Science & Informatics
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Publisher:	Springer
ISBN:	9783030929671
Date of First Compliant Deposit:	11 March 2024
Last Modified:	11 Mar 2024 16:48
URI:	https://orca.cardiff.ac.uk/id/eprint/166985

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