Are sequential patterns shareable? Ensuring individuals’ privacy

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-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 languageEnglish
Title of host publicationModeling decisions for artificial intelligence
Subtitle of host publication18th International Conference, MDAI 2021, Umeå, Sweden, September 27–30, 2021, Proceedings
EditorsVicenç Torra, Yasuo Narukawa
PublisherSpringer Science and Business Media Deutschland GmbH
Pages28-39
Number of pages12
ISBN (Electronic)978-3-030-85529-1
ISBN (Print)978-3-030-85528-4
DOIs
StatePublished - 20 Sep 2021
EventInternational Conference on Modeling Decisions for Artificial Intelligence - Virtual, Online
Duration: 27 Sep 202130 Sep 2021
Conference number: 18th

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12898 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

ConferenceInternational Conference on Modeling Decisions for Artificial Intelligence
Abbreviated titleMDAI
CityVirtual, Online
Period27/09/2130/09/21

Bibliographical note

Publisher Copyright:
© 2021, Springer Nature Switzerland AG.

Keywords

  • Data privacy
  • Edge-differential privacy
  • Sequential pattern mining
  • Uniqueness

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