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 \(\epsilon \)-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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Acknowledgements
This research was partly supported by the Spanish Government under projects RTI2018-095094-B-C21 and RTI2018-095094-B-C22 “CONSENT”.
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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: Torra, V., Narukawa, Y. (eds) Modeling Decisions for Artificial Intelligence. MDAI 2021. Lecture Notes in Computer Science(), vol 12898. Springer, Cham. https://doi.org/10.1007/978-3-030-85529-1_3
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