Revisiting online anonymization algorithms to ensure location privacy

Miguel Nunez-del-Prado, Jordi Nin

Research output: Contribution to journalArticle in a journalpeer-review

3 Scopus citations

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
Pages (from-to)15097-15108
Number of pages12
JournalJournal of Ambient Intelligence and Humanized Computing
Volume14
Issue number11
Early online date3 Jul 2019
DOIs
StatePublished - Nov 2023

Bibliographical note

Publisher Copyright:
© 2019, Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords

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

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