Are sequential patterns shareable? Ensuring individuals’ privacy

Miguel Nunez-del-Prado, Julián Salas, Hugo Alatrista-Salas, Yoshitomi Maehara-Aliaga, David Megías

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

Individuals’ actions like smartphone usage, internet shopping, bank card transaction, watched movies can all be represented in form of sequences. Accordingly, these sequences have meaningful frequent temporal patterns that scientist and companies study to understand different phenomena and business processes. Therefore, we tend to believe that patterns are de-identified from individuals’ identity and safe to share for studies. Nevertheless, we show, through unicity tests, that the combination of different patterns could act as a quasi-identifier causing a privacy breach, revealing private patterns. To solve this problem, we propose to use ϵ -differential privacy over the extracted patterns to add uncertainty to the association between the individuals and their true patterns. Our results show that its possible to reduce significantly the privacy risk conserving data utility.

Idioma originalInglés
Título de la publicación alojadaModeling decisions for artificial intelligence
Subtítulo de la publicación alojada18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings
EditoresVicenç Torra, Yasuo Narukawa
EditorialSpringer Science and Business Media Deutschland GmbH
Páginas28-39
Número de páginas12
ISBN (versión digital)978-3-030-85529-1
ISBN (versión impresa)978-3-030-85528-4
DOI
EstadoPublicada - 20 sep 2021
EventoInternational Conference on Modeling Decisions for Artificial Intelligence - Virtual, Online
Duración: 27 sep 202130 sep 2021
Número de conferencia: 18th

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen12898 LNAI
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

ConferenciaInternational Conference on Modeling Decisions for Artificial Intelligence
Título abreviadoMDAI
CiudadVirtual, Online
Período27/09/2130/09/21

Nota bibliográfica

Funding Information:
Acknowledgements. This research was partly supported by the Spanish Government under projects RTI2018-095094-B-C21 and RTI2018-095094-B-C22 “CONSENT”.

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

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