Privacy Threat Model for IoT

Jain, Shelendra Kumar; Kesswani, Nishtha

doi:10.1007/978-3-030-39875-0_30

Cited by 2 publications

(2 citation statements)

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“…Total privacy weight P is calculated using Eq. (6). In this equation, N e (D i ) is the number of data or data packages (D i ) that use higher-level security mode, and N n (D i ) is the number of data or data packages that use lower-level security mode.…”

Section: Related Work and Motivationmentioning

confidence: 99%

“…IoT applications like smart city, smart healthcare systems, smart building, smart transport and smart environment [3], industrial, agriculture, supply chain management [4], smart B Shelendra Kumar Jain shelendra23@hotmail.com 1 Department of Computer Science, Central University of Rajasthan, NH-8, Bandar Sindri, Dist-Ajmer, Rajasthan 305817, India retail, location-based services, etc. may deal with sensitive data such as health information, financial information [5], location footprints, Personally Identifiable Information (PII) [6], data of personal life, etc. Data deluge from billions of entities producing information is a significant threat to privacy [7] (Fig.…”

Section: Introductionmentioning

confidence: 99%

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A noise-based privacy preserving model for Internet of Things

Jain

Kesswani

2021

Complex Intell. Syst.

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With the ever-increasing number of devices, the Internet of Things facilitates the connection between the devices in the hyper-connected world. As the number of interconnected devices increases, sensitive data disclosure becomes an important issue that needs to be addressed. In order to prevent the disclosure of sensitive data, effective and feasible privacy preservation strategies are necessary. A noise-based privacy-preserving model has been proposed in this article. The components of the noise-based privacy-preserving model include Multilevel Noise Treatment for data collection; user preferences-based data classifier to classify sensitive and non-sensitive data; Noise Removal and Fuzzification Mechanism for data access and user-customized privacy preservation mechanism. Experiments have been conducted to evaluate the performance and feasibility of the proposed model. The results have been compared with existing approaches. The experimental results show an improvement in the proposed noise-based privacy-preserving model in terms of computational overhead. The comparative analysis indicates that the proposed model without the fuzzifier has around 52–77% less computational overhead than the Data access control scheme and 46–70% less computational overhead compared to the Dynamic Privacy Protection model. The proposed model with the fuzzifier has around 48–73% less computational overhead compared to the Data access control scheme and 31–63% less computational overhead compared to the Dynamic Privacy Protection model. Furthermore, the privacy analysis has been done with the relevant approaches. The results indicate that the proposed model can customize privacy as per the users’ preferences and at the same time takes less execution time which reduces the overhead on the resource constraint IoT devices.

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Section: Related Work and Motivationmentioning

confidence: 99%