Pervasive Context Sharing in Magpie: Adaptive Trust-Based Privacy Protection

Liu, Chenguang; Julien, Christine

doi:10.1007/978-3-319-29003-4_8

Cited by 6 publications

(3 citation statements)

References 27 publications

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“…In the IoT domain, several context-oriented privacy requirements (Bauer et al, 2013;Liu & Julien, 2015;Skillen et al, 2012) have been implemented to protect user privacy. In their systematic review, Gharib et al (2020) identified some concepts and relationships for context-oriented privacy requirements as follows: (i) Anonymity: The identity of a data subject should not be identifiable within a set of other subjects, that is, a data subject cannot be sufficiently identified by others.…”

Section: Context-oriented Privacy Requirementsmentioning

confidence: 99%

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A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

Ogunniye

Kökciyan

2023

jair

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People are interacting with online systems all the time. In order to use the services being provided, they give consent for their data to be collected. This approach requires too much human effort and is impractical for systems like Internet-of-Things (IoT) where human-device interactions can be large. Ideally, privacy assistants can help humans make privacy decisions while working in collaboration with them. In our work, we focus on the identification and representation of privacy requirements in IoT to help privacy assistants better understand their environment. In recent years, more focus has been on the technical aspects of privacy. However, the dynamic nature of privacy also requires a representation of social aspects (e.g., social trust). In this survey paper, we review the privacy requirements represented in existing IoT ontologies. We discuss how to extend these ontologies with new requirements to better capture privacy, and we introduce case studies to demonstrate the applicability of the novel requirements.

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Section: Context-oriented Privacy Requirementsmentioning

confidence: 99%

“…Magpie (Liu & Julien, 2015) Trust-adaptive and privacypreserving approach for context-sharing applications.…”

Section: Analysis Of the Selected Ontologiesmentioning

confidence: 99%

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

Ogunniye

Kökciyan

2023

jair

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“…It is an approach in which context sharing is provided by opportunistically connections between entities [52] . Magpie works in pervasive computing environments, sharing context information of mobile and heterogeneous devices.…”

Section: Magpiementioning

confidence: 99%

Context information sharing for the Internet of Things: A survey

et al. 2020

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A fuzzy‐based trust model for flying ad hoc networks (FANETs)

Singh

Verma

2018

Int J Communication

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SummaryThe use of unmanned aerial vehicles has significantly increased for forming an ad hoc network owing to their ability to perform in exciting environment such as armed attacks, border surveillance, disaster management, rescue operation, and transportation. Such types of ad hoc networks are popularly known as flying ad hoc networks (FANETs). The FANET nodes have 2 prominent characteristics—collaboration and cooperation. Trust plays an important role in predicting the behavior of such nodes. Researchers have proposed various methods (direct and indirect) for calculation of the trust value of a given node in ad hoc networks, especially in mobile ad hoc networks and vehicular ad hoc networks. The major characteristic that differentiates a FANET from other ad hoc networks is the velocity of the node; as a result, there are frequent losses in connection and topology change. Therefore, the existing methods of trust calculation are not efficient and effective. In this paper, a fuzzy‐based novel trust model has been proposed to handle the behavioral uncertainty of FANET nodes. Nodes are classified using a multicriteria fuzzy classification method based on node's behavior and performance in the fuzzy and complex environment. Quality of service and social parameter (recommendation) are considered for evaluating the trust value of each node to segregate the selfish and malicious nodes. With the node classification, FANET nodes are rewarded or punished to transform node behavior into a trust value. Compared with the existing trust techniques, the simulation results show that the proposed model has better adaptability, accuracy, and performance in FANETs.

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Pervasive Context Sharing in Magpie: Adaptive Trust-Based Privacy Protection

Cited by 6 publications

References 27 publications

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

A Survey on Understanding and Representing Privacy Requirements in the Internet-of-Things

Context information sharing for the Internet of Things: A survey

A fuzzy‐based trust model for flying ad hoc networks (FANETs)

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