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2017 | OriginalPaper | Chapter

Mining Representative Patterns Under Differential Privacy

Authors : Xiaofeng Ding, Long Chen, Hai Jin

Published in: Web Information Systems Engineering – WISE 2017

Publisher: Springer International Publishing

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Abstract

Representative frequent pattern mining from a transaction dataset has been well studied in both the database and the data mining community for many years. One popular scenario is that if the input dataset contains private information, publishing representative patterns may pose great threats to individual’s privacy. In this paper, we study the subject of mining representative patterns under the differential privacy model. We propose a method that combines RPlocal with differential privacy to mine representative patterns. We analyze the breach of privacy in RPlocal, and utilize the differential privacy to protect the private information of transaction dataset. Through formal privacy analysis, we prove that our proposed algorithm satisfies \(\epsilon \)-differential privacy. Extensive experimental results on real datasets reveal that our algorithm produces similar number of representative patterns compared to RPlocal.

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Metadata
Title
Mining Representative Patterns Under Differential Privacy
Authors
Xiaofeng Ding
Long Chen
Hai Jin
Copyright Year
2017
Book
Web Information Systems Engineering – WISE 2017

Print ISBN: 978-3-319-68785-8

Electronic ISBN: 978-3-319-68786-5

Copyright Year: 2017

DOI
https://doi.org/10.1007/978-3-319-68786-5_23

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