DP-MCDBSCAN: Differential Privacy Preserving Multi-Core DBSCAN Clustering for Network User Data

The proliferation of ubiquitous Internet and mobile devices has brought about the exponential growth of individual data in big data era. The network user data has been confronted with serious privacy concerns for extracting valuable information during the process of data mining. Differential privacy...

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Bibliographic Details
Published in:IEEE access 2018-01, Vol.6, p.21053-21063
Main Authors: Ni, Lina, Li, Chao, Wang, Xiao, Jiang, Honglu, Yu, Jiguo
Format: Article
Language:English
Subjects:
Algorithms
Clustering
Clustering algorithms
Data mining
Data privacy
differential privacy
Distributed databases
Electronic devices
Laplace noise
MCDBSCAN clustering
Privacy
Privacy preservation
Publishing
Random noise
Sensitivity
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Summary:The proliferation of ubiquitous Internet and mobile devices has brought about the exponential growth of individual data in big data era. The network user data has been confronted with serious privacy concerns for extracting valuable information during the process of data mining. Differential privacy preservation is a new paradigm independent of the adversaries' prior knowledge, which protects sensitive data while maintaining certain statistical properties by adding random noise. In this paper, we put forward a differential privacy preservation multiple cores DBSCAN clustering schema based on the powerful differential privacy and DBSCAN algorithm for network user data to effectively leverage the privacy leakage issue in the process of data mining, enhancing data clustering efficaciously by adding Laplace noise. We perform extensive theoretical analysis and simulations to evaluate our schema and the results show better efficiency, accuracy, and privacy preservation effect than previous schemas.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2824798