Lightweight Privacy-preserving Raw Data Publishing Scheme
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Journal Title
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Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
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Author
Date
2021-12
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Mcode
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Language
en
Pages
6
Series
IEEE Transactions on Emerging Topics in Computing
Abstract
Data publishing or data sharing is an important part of analyzing network environments and improving the Quality of Service (QoS) in the Internet of Things (IoT). In order to stimulate data providers (i.e., IoT end-users) to contribute their data, privacy requirement is necessary when data is collected and published. In traditional privacy preservation techniques, such as k-anonymity, data aggregation and differential privacy, data is modified, aggregated, or added noise, the utility of the published data are reduced. Privacy-preserving raw data publishing is a more valuable solution, and nn-source anonymity based raw data collection is most promising by delinking raw data and their sources. In this article, a lightweight raw data collection scheme for publishing is proposed, in which the rawness and the unlinkability of published data are all really guaranteed with Shamir's secret sharing, and shuffling algorithm. Moreover, it is lightweight and practical for the IoT environment by the performance evaluation.Description
Keywords
Cryptography, Data centers, data collection, Data collection, Data models, Data privacy, Internet of Things, lightweight, privacy, Publishing, rawness, unlinkability
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Citation
Chen, J, Liu, G & Liu, Y 2021, ' Lightweight Privacy-preserving Raw Data Publishing Scheme ', IEEE Transactions on Emerging Topics in Computing, vol. 9, no. 4, pp. 2170-2174 . https://doi.org/10.1109/TETC.2020.2974183