Lightweight Privacy-preserving Raw Data Publishing Scheme

Jingxue Chen, Gao Liu, Yining Liu

Research output: Contribution to journalArticleScientificpeer-review

23 Citations (Scopus)
1 Downloads (Pure)

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 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.

Original languageEnglish
Number of pages6
JournalIEEE Transactions on Emerging Topics in Computing
DOIs
Publication statusE-pub ahead of print - 2020
MoE publication typeA1 Journal article-refereed

Keywords

  • Cryptography
  • Data centers
  • data collection
  • Data collection
  • Data models
  • Data privacy
  • Internet of Things
  • lightweight
  • privacy
  • Publishing
  • rawness
  • unlinkability

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