Revisiting online anonymization algorithms to ensure location privacy

Miguel Nunez-del-Prado, Jordi Nin

Research output: Indexed journal article Articlepeer-review

5 Citations (Scopus)

Abstract

Individuals are continually observed and monitored by many location-based services, such as social networks, telecommunication companies, mobile networks, etc. The resulting streams of data, which are usually analyzed in real time, can reveal sensitive information about individuals, e.g. home/work location or private mobility patterns. Therefore, there is a need for stream processing algorithms able to anonymize datasets in real time to ensure certain privacy guarantees, but at the same time keeping a low error. In this paper, we describe how statistical disclosure control (SDC) methods can be applied to a Call Detail Record (CDR) database in a stream fashion to mask location information efficiently. Besides, we also provide some experimental results over a real database.

Original languageEnglish
JournalJournal of Ambient Intelligence and Humanized Computing
DOIs
Publication statusAccepted/In press - 2019
Externally publishedYes

Keywords

  • CDR privacy
  • Location privacy
  • Online privacy
  • Stream anonymization

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