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Lightweight Privacy-Preserving Raw Data Publishing Scheme - 2020

Research Area:  Internet of Things

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 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 Shamirs secret sharing, and shuffling algorithm. Moreover, it is lightweight and practical for the IoT environment by the performance evaluation.

Author(s) Name:  Jingxue Chen; Gao Liu; Yining Liu

Journal name:   IEEE Transactions on Emerging Topics in Computing

Conferrence name:  

Publisher name:  IEEE

DOI:  10.1109/TETC.2020.2974183

Volume Information:  ( Volume: 9, Issue: 4, Oct.-Dec. 1 2021) Page(s): 2170 - 2174