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 |
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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 | |
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) |
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Volume | 12898 LNAI |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | International Conference on Modeling Decisions for Artificial Intelligence |
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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