Evaluating Holistic Privacy Risk Posed by Smart Home Ecosystem: A Capability-Oriented Model Accommodating Epistemic Uncertainty and Wisdom of Crowds

Evaluating the holistic privacy risk (HPR) presented by a smart home ecosystem (SHE), encompassing both internal and external entities that may be targeted by different adversaries seeking to compromise users' privacy, can enhance the comprehensive understanding of the privacy risk landscape wi...

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Bibliographic Details
Published in:IEEE transactions on engineering management 2024, Vol.71, p.5372-5390
Main Authors: Chang, Jian-Peng, Zheng, Hong-Liang, Mardani, Abbas, Pedrycz, Witold, Chen, Zhen-Song
Format: Article
Language:English
Subjects:
Data privacy
Ecosystems
Epistemic uncertainty
Epistemology
holistic privacy risk (HPR) assessment
Linguistics
Privacy
Risk assessment
Risk management
Smart buildings
smart home ecosystem (SHE)
Smart homes
Smart houses
Uncertainty
wisdom of crowds (WoC)
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Summary:Evaluating the holistic privacy risk (HPR) presented by a smart home ecosystem (SHE), encompassing both internal and external entities that may be targeted by different adversaries seeking to compromise users' privacy, can enhance the comprehensive understanding of the privacy risk landscape within the SHE. This matter is influenced by the complexity of risk surroundings, the diverse perspectives of users toward privacy, and the lack of historical data. Unfortunately, existing literature falls short in addressing these factors. To fill the gap, this article develops an innovative capability-oriented model that accommodates epistemic uncertainty and wisdom of crowds (WoC), designed to assist smart home device manufacturers in accurately assessing HPR posed by their SHEs. The model presents a method for representing subjective judgments that captures epistemic uncertainty and a technique for weighting individual judgments to mitigate overconfidence bias, thus effectively harnessing WoC. In addition, this model features two specialized methods: one for quantifying HPR and another for prioritizing associated single risks, both tailored to operate effectively within uncertain context. These innovative methods are versatile and can be applied to various risk assessment scenarios, especially where historical data are not available. The practicality and effectiveness of our model are demonstrated through a detailed case study.
ISSN:0018-9391
1558-0040
DOI:10.1109/TEM.2024.3351703