Mining Representative Patterns Under Differential Privacy

  • Xiaofeng DingEmail author
  • Long Chen
  • Hai Jin
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)


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.


Representative pattern Differential privacy RPlocal 



This work is supported by the NSFC under grant No. 61472148 and the National Basic Research Program of China (973 Program) under grant No. 2014CB340600.


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Service Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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