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Article

A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns

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Peru Research Development, and Innovation (PERU IDI), Lima 15047, Peru
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Instituto de Investigación, Universidad Andina del Cusco, Cusco 08006, Peru
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Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili (URV), 43007 Tarragona, Spain
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Center for Cybersecurity Research of Catalonia (CYBERCAT), 08860 Barcelona, Spain
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Science and Engineering School, Pontificia Universidad Católica del Perú (PUCP), Lima 5088, Peru
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Internet Interdisciplinary Institute (IN3), Universitat Oberta de Catalunya (UOC), 08860 Barcelona, Spain
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Authors to whom correspondence should be addressed.
Academic Editor: Federico Divina
Appl. Sci. 2022, 12(4), 2131; https://doi.org/10.3390/app12042131
Received: 24 December 2021 / Revised: 3 February 2022 / Accepted: 14 February 2022 / Published: 18 February 2022
(This article belongs to the Section Computing and Artificial Intelligence)
Currently, individuals leave a digital trace of their activities when they use their smartphones, social media, mobile apps, credit card payments, Internet surfing profile, etc. These digital activities hide intrinsic usage patterns, which can be extracted using sequential pattern algorithms. Sequential pattern mining is a promising approach for discovering temporal regularities in huge and heterogeneous databases. These sequences represent individuals’ common behavior and could contain sensitive information. Thus, sequential patterns should be sanitized to preserve individuals’ privacy. Hence, many algorithms have been proposed to accomplish this task. However, these techniques add noise to the candidate support before they are validated as, frequently, and thus, they cannot be applied without having access to all the users’ sequences data. In this paper, we propose a differential privacy graph-based technique for publishing frequent sequential patterns. It is applied at the post-processing stage; hence it may be used to protect frequent sequential patterns after they have been extracted, without the need to access all the users’ sequences. To validate our proposal, we performed a detailed assessment of its utility as a pattern mining algorithm and calculated the impact of the sanitization mechanism on a recommender system. We further evaluated its information loss disclosure risk and performed a comparison with the DP-FSM algorithm. View Full-Text
Keywords: sequential pattern mining; differential privacy; frequent pattern mining; edge differential privacy; graph differential privacy; anonymization of big data sequential pattern mining; differential privacy; frequent pattern mining; edge differential privacy; graph differential privacy; anonymization of big data
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MDPI and ACS Style

Nunez-del-Prado, M.; Maehara-Aliaga, Y.; Salas, J.; Alatrista-Salas, H.; Megías, D. A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns. Appl. Sci. 2022, 12, 2131. https://doi.org/10.3390/app12042131

AMA Style

Nunez-del-Prado M, Maehara-Aliaga Y, Salas J, Alatrista-Salas H, Megías D. A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns. Applied Sciences. 2022; 12(4):2131. https://doi.org/10.3390/app12042131

Chicago/Turabian Style

Nunez-del-Prado, Miguel, Yoshitomi Maehara-Aliaga, Julián Salas, Hugo Alatrista-Salas, and David Megías. 2022. "A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns" Applied Sciences 12, no. 4: 2131. https://doi.org/10.3390/app12042131

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