Anonymizing mobility data using semantic cloaking

Omer Barak, Gabriella Cohen, Eran Toch

Research output: Contribution to journalArticlepeer-review


The prevalence of mobile phones has led to an explosion in the amounts of human mobility data stored in the cloud. It has been shown that seemingly anonymized location datasets are highly susceptible for re-identification, and may not provide enough privacy protection. In this paper we quantitatively show how semantic cloaking, the application of semantic labeling to achieve anonymization, can improve the privacy of a mobility dataset for use cases where location coordinates can be replaced by semantic categories. We develop a semantic labeling framework, apply it and evaluate it using the dataset uniqueness (ϵ) measure. Our experiments show an improvement in uniqueness ranging between two- and twenty two-fold in comparison to the original, naively anonymized, dataset.

Original languageEnglish
Pages (from-to)102-112
Number of pages11
JournalPervasive and Mobile Computing
StatePublished - 1 Jun 2016


  • Human mobility
  • Location privacy
  • Semantic cloaking
  • Semantic labeling
  • k-anonymity

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Hardware and Architecture
  • Computer Science Applications
  • Computer Networks and Communications


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