Are sequential patterns shareable?: Ensuring individuals’ privacy

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

doi:10.1007/978-3-030-85529-1_3

Are sequential patterns shareable? Ensuring individuals’ privacy

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

Universidad del Pacífico

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

1 Cita (Scopus)

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 original	Inglés
Título de la publicación alojada	Modeling decisions for artificial intelligence
Subtítulo de la publicación alojada	18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings
Editores	Vicenç Torra, Yasuo Narukawa
Editorial	Springer Science and Business Media Deutschland GmbH
Páginas	28-39
Número de páginas	12
ISBN (versión digital)	978-3-030-85529-1
ISBN (versión impresa)	978-3-030-85528-4
DOI	https://doi.org/10.1007/978-3-030-85529-1_3
Estado	Publicada - 20 set. 2021
Evento	International Conference on Modeling Decisions for Artificial Intelligence - Virtual, Online Duración: 27 set. 2021 → 30 set. 2021 Número de conferencia: 18th

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	12898 LNAI
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	International Conference on Modeling Decisions for Artificial Intelligence
Título abreviado	MDAI
Ciudad	Virtual, Online
Período	27/09/21 → 30/09/21

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.

ODS de las Naciones Unidas

Este resultado contribuye a los siguientes Objetivos de Desarrollo Sostenible

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10.1007/978-3-030-85529-1_3

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Nunez-del-Prado, M., Salas, J., Alatrista-Salas, H., Maehara-Aliaga, Y., & Megías, D. (2021). Are sequential patterns shareable? Ensuring individuals’ privacy. En V. Torra, & Y. Narukawa (Eds.), Modeling decisions for artificial intelligence: 18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings (pp. 28-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12898 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-030-85529-1_3

Nunez-del-Prado, Miguel ; Salas, Julián ; Alatrista-Salas, Hugo et al. / Are sequential patterns shareable? Ensuring individuals’ privacy. Modeling decisions for artificial intelligence: 18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings. editor / Vicenç Torra ; Yasuo Narukawa. Springer Science and Business Media Deutschland GmbH, 2021. pp. 28-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{9ab93eee9783423795f3ca07f735e27d,

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abstract = "Individuals{\textquoteright} 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{\textquoteright} 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.",

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Nunez-del-Prado, M, Salas, J, Alatrista-Salas, H, Maehara-Aliaga, Y & Megías, D 2021, Are sequential patterns shareable? Ensuring individuals’ privacy. En V Torra & Y Narukawa (eds.), Modeling decisions for artificial intelligence: 18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12898 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 28-39, International Conference on Modeling Decisions for Artificial Intelligence, Virtual, Online, 27/09/21. https://doi.org/10.1007/978-3-030-85529-1_3

Are sequential patterns shareable? Ensuring individuals’ privacy. / Nunez-del-Prado, Miguel; Salas, Julián; Alatrista-Salas, Hugo et al.
Modeling decisions for artificial intelligence: 18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings. ed. / Vicenç Torra; Yasuo Narukawa. Springer Science and Business Media Deutschland GmbH, 2021. p. 28-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12898 LNAI).

Producción científica: Capítulo del libro/informe/acta de congreso › Contribución a la conferencia › revisión exhaustiva

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Nunez-del-Prado M, Salas J, Alatrista-Salas H, Maehara-Aliaga Y, Megías D. Are sequential patterns shareable? Ensuring individuals’ privacy. En Torra V, Narukawa Y, editores, Modeling decisions for artificial intelligence: 18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings. Springer Science and Business Media Deutschland GmbH. 2021. p. 28-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-85529-1_3