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http://hdl.handle.net/10609/142866
Title: A graph-based differentially private algorithm for mining frequent sequential patterns
Author: Megías Jiménez, David  
Alatrista Salas, Hugo
Salas Piñón, Julián
Maehara Aliaga, Yoshitomi
Núñez del Prado, Miguel
Others: Universitat Oberta de Catalunya
Pontificia Universidad Católica del Perú
Peru Research Development, and Innovation
Universidad Andina del Cusco
Universitat Rovira i Virgili
Keywords: sequential pattern mining
differential privacy
frequent pattern mining
edge differential privacy
graph differential privacy
anonymization of big data
Issue Date: 18-Feb-2022
Publisher: Applied Sciences
Citation: Nunez-del-Prado, M., Maehara-Aliaga, Y., Salas, J., Alatrista-Salas, H., & Megías, D. (2022). A Graph-Based Differentially Private Algorithm for Mining Frequent Sequential Patterns. Applied Sciences, 12(4), 2131. MDPI AG. Retrieved from http://dx.doi.org/10.3390/app12042131
Published in: 12;4
Project identifier: info:eu-repo/grantAgreement/ES/RTI2018-095094-B-C21
info:eu-repo/grantAgreement/ES/RTI2018-095094-B-C22
Also see: http://doi.org/10.3390/app12042131
Abstract: 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.
Language: English
URI: http://hdl.handle.net/10609/142866
ISSN: 2076-3417MIAR
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