Privacy Preservation and Inference with Minimal Mobility Information

Julián Salas, Miguel Nunez-del-Prado

Resultado de la investigación: Capítulo del libro/informe/acta de congresoCapítulo de libro

Resumen

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.
Idioma originalInglés
Título de la publicación alojadaInformation Management and Big Data - 6th International Conference, SIMBig 2019, Proceedings
EditoresJuan Antonio Lossio-Ventura, Nelly Condori-Fernandez, Jorge Carlos Valverde-Rebaza
Páginas129-142
Número de páginas14
ISBN (versión digital)9783030461393
DOI
EstadoPublicada - 1 ene. 2020
EventoCommunications in Computer and Information Science -
Duración: 1 ene. 2020 → …

Serie de la publicación

NombreCommunications in Computer and Information Science
Volumen1070 CCIS
ISSN (versión impresa)1865-0929
ISSN (versión digital)1865-0937

Conferencia

ConferenciaCommunications in Computer and Information Science
Período1/01/20 → …

Nota bibliográfica

Funding Information:
Acknowledgement. This work was supported by the Spanish Government, in part under Grant RTI2018-095094-B-C22 “CONSENT”, and in part under Grant TIN2014-57364-C2-2-R “SMARTGLACIS.”

Publisher Copyright:
© Springer Nature Switzerland AG 2020.

Palabras clave

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

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