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AuthorRazaq, Mian Muaz
AuthorTak, Byungchul
AuthorPeng, Limei
AuthorGuizani, Mohsen
Available date2022-10-12T08:17:19Z
Publication Date2022-02-01
Publication NameIEEE Transactions on Computational Social Systems
Identifierhttp://dx.doi.org/10.1109/TCSS.2020.3047382
CitationRazaq, M. M., Tak, B., Peng, L., & Guizani, M. (2021). Privacy-aware collaborative task offloading in fog computing. IEEE Transactions on Computational Social Systems, 9(1), 88-96.‏
URIhttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85099732396&origin=inward
URIhttp://hdl.handle.net/10576/35049
AbstractNumerous new applications have been proliferated with the mature of 5G, which generates a large number of latency-sensitive and computationally intensive mobile data requests. The real-time requirement of these mobile data has been accommodated well by fog computing in the past few years, mainly through offloading tasks to fog nodes in the vicinity. On the other hand, the user-privacy hidden in the Internet-of-Things (IoT) data has not been sufficiently considered in the presence of insecure fog nodes. It is risky to offload an entire mission-critical task to just one fog node or several fog nodes owned by the same service provider (SP), especially when the SP is marked with low-security credit and tends to collect data information of users for malicious use. To address this issue, we classify IoT user tasks based on their security requirements, divide them into different numbers of smaller fragments, and, finally, offload the segments of a task to multiple fog nodes owned by the same or various SPs according to their security requirements. The selected fog nodes will collaboratively serve the divided fragments to avoid the possible damage caused by the leak of sensitive data due to compromised fog nodes of malicious SPs. For this, we propose an integer linear programming (ILP) model and a dynamic programming algorithm to maximize the number of successfully served IoT data tasks with satisfactory security requirements while minimizing the end-to-end transmission delay. The numerical results show that the proposed ILP model and algorithm can significantly increase the successful provisioning ratio for tasks with high-security requirements.
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectFog computing
Internet of Things (IoT)
user privacy
TitlePrivacy-Aware Collaborative Task Offloading in Fog Computing
TypeArticle
Pagination88-96
Issue Number1
Volume Number9
dc.accessType Abstract Only


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