Privacy Preservation and Inference with Minimal Mobility Information

Julián Salas, Miguel Nunez-del-Prado

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


There is much debate about the challenge to anonymize a large amount of information obtained in big data scenarios. Besides, it is even harder considering inferences from data may be used as additional adversary knowledge. This is the case of geo-located data, where the Points of Interest (POIs) may have additional information that can be used to link them to a user’s real identity. However, in most cases, when a model of the raw data is published, this processing protects up to some point the privacy of the data subjects by minimizing the published information. In this paper, we measure the privacy obtained by the minimization of the POIs published when we apply the Mobility Markov Chain (MMC) model, which extracts the most important POIs of an individual. We consider the gender inferences that an adversary may obtain from publishing the MMC model together with additional information such as the gender or age distribution of each POI, or the aggregated gender distribution of all the POIs visited by a data subject. We measure the unicity obtained after applying the MMC model, and the probability that an adversary that knows some POIs in the data before processing may be able to link them with the POIs published after the MMC model. Finally, we measure the anonymity lost when adding the gender attribute to the side knowledge of an adversary that has access to the MMC model. We test our algorithms on a real transaction database.
Original languageEnglish
Title of host publicationInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditorsJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Place of PublicationChan
Number of pages14
ISBN (Electronic)9783030461393
ISBN (Print)9783030461393
StatePublished - 1 Jan 2020
EventCommunications in Computer and Information Science -
Duration: 1 Jan 2020 → …

Publication series

NameCommunications in Computer and Information Science
Volume1070 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


ConferenceCommunications in Computer and Information Science
Period1/01/20 → …

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2020.


  • Data protection regulation
  • Gender inference
  • Geo-located data privacy
  • Mobility Markov Chain


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