Beyond Value Perturbation: Local Differential Privacy in the Temporal Setting

Qingqing Ye, Haibo Hu, Ninghui Li, Xiaofeng Meng, Huadi Zheng, Haotian Yan

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

12 Citations (Scopus)


Time series has numerous application scenarios. However, since many time series data are personal data, releasing them directly could cause privacy infringement. All existing techniques to publish privacy-preserving time series perturb the values while retaining the original temporal order. However, in many value-critical scenarios such as health and financial time series, the values must not be perturbed whereas the temporal order can be perturbed to protect privacy. As such, we propose "local differential privacy in the temporal setting"(TLDP) as the privacy notion for time series data. After quantifying the utility of a temporal perturbation mechanism in terms of the costs of a missing, repeated, empty, or delayed value, we propose three mechanisms for TLDP. Through both analytical and empirical studies, we show the last one, Threshold mechanism, is the most effective under most privacy budget settings, whereas the other two baseline mechanisms fill a niche by supporting very small or large privacy budgets.

Original languageEnglish
Title of host publicationINFOCOM 2021 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9780738112817
Publication statusPublished - 10 May 2021
Event40th IEEE Conference on Computer Communications, INFOCOM 2021 - Vancouver, Canada
Duration: 10 May 202113 May 2021

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference40th IEEE Conference on Computer Communications, INFOCOM 2021


  • Data sanitization
  • Local differential privacy
  • Time series data

ASJC Scopus subject areas

  • Computer Science(all)
  • Electrical and Electronic Engineering


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