Lightweight Privacy-preserving Raw Data Publishing Scheme

Jingxue Chen, Gao Liu, Yining Liu

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

26 Sitaatiot (Scopus)
8 Lataukset (Pure)


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 incentives 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 paper, 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.

JulkaisuIEEE Transactions on Emerging Topics in Computing
DOI - pysyväislinkit
TilaSähköinen julkaisu (e-pub) ennen painettua julkistusta - 2020
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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