期刊
IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING
卷 9, 期 4, 页码 2170-2174出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TETC.2020.2974183
关键词
Data collection; privacy; rawness; unlinkability; lightweight
资金
- Natural Science Foundation of China [61662016, 62072133]
- Key projects of Guangxi Natural Science Foundation [2018GXNSFDA281040]
- Study Abroad Program for Graduate Student of Guilin University of Electronic Technology [GDYX2019008]
This article proposes a lightweight raw data collection scheme for publishing, which ensures the rawness and unlinkability of published data through Shamir's secret sharing and shuffling algorithm, making it suitable and practical for the IoT environment.
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 n-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.
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