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Machine learning differential privacy with multifunctional aggregation in a fog computing architecture

Yang, Mengmeng, Zhu, Tianqing, Liu, Bo, Xiang, Yang and Zhou, Wanlei 2018, Machine learning differential privacy with multifunctional aggregation in a fog computing architecture, IEEE Access, vol. 6, pp. 17119-17129, doi: 10.1109/ACCESS.2018.2817523.

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Title Machine learning differential privacy with multifunctional aggregation in a fog computing architecture
Author(s) Yang, Mengmeng
Zhu, TianqingORCID iD for Zhu, Tianqing orcid.org/0000-0003-3411-7947
Liu, BoORCID iD for Liu, Bo orcid.org/0000-0002-3603-6617
Xiang, YangORCID iD for Xiang, Yang orcid.org/0000-0001-5252-0831
Zhou, Wanlei
Journal name IEEE Access
Volume number 6
Start page 17119
End page 17129
Total pages 11
Publisher IEEE
Place of publication Piscataway, N.J.
Publication date 2018-03-19
ISSN 2169-3536
Keyword(s) data aggregation
differential privacy
fog computing
Science & Technology
Technology
Computer Science, Information Systems
Engineering, Electrical & Electronic
Telecommunications
Computer Science
Engineering
SCHEME
EFFICIENT
Summary Data aggregation plays an important role in the Internet of Things, and its study and analysis has resulted in a range of innovative services and benefits for people. However, the privacy issues associated with raw sensory data raise significant concerns due to the sensitive nature of the user information it often contains. Thus, numerous schemes have been proposed over the last few decades to preserve the privacy of users' data. Most methods are based on encryption technology, which is computationally and communicationally expensive. In addition, most methods can only handle a single aggregation function. Therefore, in this paper, we propose a multifunctional data aggregation method with differential privacy. The method is based on machine learning and can support a wide range of statistical aggregation functions, including additive and non-additive aggregation. It operates within a fog computing architecture, which extends cloud computing to the edge of the network, alleviating much of the computational burden on the cloud server. And, by only reporting the results of the aggregation to the server, communication efficiency is improved. Extensive experimental results show that the proposed method not only answers flexible aggregation queries that meet diversified aggregation goals, but also produces aggregation results with high accuracy.
Language eng
DOI 10.1109/ACCESS.2018.2817523
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2018, IEEE
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30113166

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.