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

Applied Sciences

David Megías¹  Julián Salas²  Yoshitomi Maehara-Aliaga³  Miguel Nunez-del-Prado³  Hugo Alatrista-Salas⁴ 

[1] Center for Cybersecurity Research of Catalonia (CYBERCAT), 08860 Barcelona, Spain;Departament d’Enginyeria Informàtica i Matemàtiques, Universitat Rovira i Virgili (URV), 43007 Tarragona, Spain;Peru Research Development, and Innovation (PERU IDI), Lima 15047, Peru;Science and Engineering School, Pontificia Universidad Católica del Perú (PUCP), Lima 5088, Peru;

关键词: sequential pattern mining;    differential privacy;    frequent pattern mining;    edge differential privacy;    graph differential privacy;    anonymization of big data;   

DOI  :  10.3390/app12042131

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown