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AuthorShen M.
AuthorTang X.
AuthorZhu L.
AuthorDu X.
AuthorGuizani M.
Available date2020-04-15T12:01:40Z
Publication Date2019
Publication NameIEEE Internet of Things Journal
ResourceScopus
ISSN23274662
URIhttp://dx.doi.org/10.1109/JIOT.2019.2901840
URIhttp://hdl.handle.net/10576/14146
AbstractMachine learning (ML) techniques have been widely used in many smart city sectors, where a huge amount of data is gathered from various (IoT) devices. As a typical ML model, support vector machine (SVM) enables efficient data classification and thereby finds its applications in real-world scenarios, such as disease diagnosis and anomaly detection. Training an SVM classifier usually requires a collection of labeled IoT data from multiple entities, raising great concerns about data privacy. Most of the existing solutions rely on an implicit assumption that the training data can be reliably collected from multiple data providers, which is often not the case in reality. To bridge the gap between ideal assumptions and realistic constraints, in this paper, we propose secureSVM, which is a privacy-preserving SVM training scheme over blockchain-based encrypted IoT data. We utilize the blockchain techniques to build a secure and reliable data sharing platform among multiple data providers, where IoT data is encrypted and then recorded on a distributed ledger. We design secure building blocks, such as secure polynomial multiplication and secure comparison, by employing a homomorphic cryptosystem, Paillier, and construct a secure SVM training algorithm, which requires only two interactions in a single iteration, with no need for a trusted third-party. Rigorous security analysis prove that the proposed scheme ensures the confidentiality of the sensitive data for each data provider as well as the SVM model parameters for data analysts. Extensive experiments demonstrates the efficiency of the proposed scheme. - 2014 IEEE.
SponsorManuscript received September 14, 2018; revised January 8, 2019; accepted February 18, 2019. Date of publication February 26, 2019; date of current version October 8, 2019. This work was supported in part by the National Key Research and Development Program of China under Grant 2018YFB0803405, in part by the National Natural Science Foundation of China under Grant 61602039 and Grant 61872041, in part by the Beijing Natural Science Foundation under Grant 4192050, and in part by CCF-Tencent Open Fund WeBank Special Funding. (Corresponding author: Liehuang Zhu.) M. Shen, X. Tang, and L. Zhu are with the School of Computer Science, Beijing Institute of Technology, Beijing 100081, China (e-mail: shenmeng@bit.edu.cn; tangguotxy@163.com; liehuangz@bit.edu.cn).
Languageen
PublisherInstitute of Electrical and Electronics Engineers Inc.
SubjectBlockchain
encrypted Internet of Things (IoT) data
homomorphic cryptosystem (HC)
machine learning (ML)
privacy protection
TitlePrivacy-Preserving Support Vector Machine Training over Blockchain-Based Encrypted IoT Data in Smart Cities
TypeArticle
Pagination7702-7712
Issue Number5
Volume Number6


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