Are Sequential Patterns Shareable? Ensuring Individuals’ Privacy

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

Producción científica: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

1 Cita (Scopus)


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 - 18th International Conference, MDAI 2021, Proceedings
EditoresVicenç Torra, Yasuo Narukawa
EditorialSpringer Science and Business Media Deutschland GmbH
Número de páginas12
ISBN (versión impresa)9783030855284
EstadoPublicada - 2021
Evento18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021 - Virtual, Online
Duración: 27 set. 202130 set. 2021

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


Conferencia18th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2021
CiudadVirtual, Online


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