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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	Modeling decisions for artificial intelligence
Subtitle of host publication	18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings
Editors	Vicenç Torra, Yasuo Narukawa
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	28-39
Number of pages	12
ISBN (Electronic)	978-3-030-85529-1
ISBN (Print)	978-3-030-85528-4
DOIs	https://doi.org/10.1007/978-3-030-85529-1_3
State	Published - 20 Sep 2021
Event	International Conference on Modeling Decisions for Artificial Intelligence - Virtual, Online Duration: 27 Sep 2021 → 30 Sep 2021 Conference number: 18th

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12898 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	International Conference on Modeling Decisions for Artificial Intelligence
Abbreviated title	MDAI
City	Virtual, Online
Period	27/09/21 → 30/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

Data privacy
Edge-differential privacy
Sequential pattern mining
Uniqueness

Access to Document

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

Cite this

Nunez-del-Prado, M., Salas, J., Alatrista-Salas, H., Maehara-Aliaga, Y., & Megías, D. (2021). Are sequential patterns shareable? Ensuring individuals’ privacy. In 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)).

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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. in 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).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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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. In Torra V, Narukawa Y, editors, 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)). https://doi.org/10.1007/978-3-030-85529-1_3