Personalized Privacy Assistant: Identity Construction and Privacy in the Internet of Things.

Internet of Things identity privacy

Journal

Entropy (Basel, Switzerland)
ISSN: 1099-4300
Titre abrégé: Entropy (Basel)
Pays: Switzerland
ID NLM: 101243874

Informations de publication

Date de publication:
26 Apr 2023
Historique:
received: 02 03 2023
revised: 13 04 2023
accepted: 14 04 2023
medline: 27 5 2023
pubmed: 27 5 2023
entrez: 27 5 2023
Statut: epublish

Résumé

Over time, the many different ways in which we collect and use data have become more complex as we communicate and interact with an ever-increasing variety of modern technologies. Although people often say they care about their privacy, they do not have a deep understanding of what devices around them are collecting their identity information, what identity information is being collected, and how that collected data will affect them. This research is dedicated to developing a personalized privacy assistant to help users regain control, understand their own identity management, and process and simplify the large amount of information from the Internet of Things (IoT). This research constructs an empirical study to obtain the comprehensive list of identity attributes that are being collected by IoT devices. We build a statistical model to simulate the identity theft and to help calculate the privacy risk score based on the identity attributes collected by IoT devices. We discuss how well each feature of our Personal Privacy Assistant (PPA) works and compare the PPA and related work to a list of fundamental features for privacy protection.

Identifiants

pubmed: 37238473
pii: e25050717
doi: 10.3390/e25050717
pmc: PMC10217614
pii:
doi:

Types de publication

Journal Article

Langues

eng

Références

BMJ. 2019 Mar 20;364:l920
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pubmed: 36904959

Auteurs

Kai-Chih Chang (KC)

Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Suzanne Barber (S)

Department of Electrical and Computer Engineering, The University of Texas at Austin, Austin, TX 78712, USA.

Classifications MeSH