Full Citation

Title: Privacy-preserving mechanism for mixed data clustering with local differential privacy

Citation Type: Journal Article

Publication Year: 2021

ISBN:

ISSN: 1532-0634

DOI: 10.1002/CPE.6503

NSFID:

PMCID:

PMID:

Abstract: In big data mining, the K-prototypes has become a popular clustering method for mixed data owing to its simplicity and efficiency. However, the data clustering process of the K-prototypes method will cause the risk of user privacy leakage because user data usually contain sensitive information. To address this issue, general solutions introduce a trusted third-party model for privacy protection in clustering analysis, but it is difficult to find a fully trusted entity in reality. In this article, we propose a local differential privacy K-prototypes (LDPK) mechanism, which does not require any trusted third party to perform privacy preprocessing on user data. Our mechanism first uses local differential privacy to disturb user data, then completes the clustering through the interaction between server and user. Furthermore, we propose a privacy protection enhancement mechanism (ELDPK) by extending the LDPK mechanism, which disturbs the user's clustering information in each iteration to protect user privacy further. Theoretical analysis proves the privacy and feasibility of our proposed scheme, and the experimental results prove that our proposed scheme can guarantee the quality of the clustering results under the premise of satisfying the local differential privacy.

Url: https://onlinelibrary.wiley.com/doi/abs/10.1002/cpe.6503

User Submitted?: No

Authors: Yuan, Liujie; Zhang, Shaobo; Zhu, Gengming; Alinani, Karim

Periodical (Full): Concurrency and Computation: Practice and Experience

Issue: 19

Volume: 35

Pages: 1-15

Data Collections: IPUMS USA

Topics: Population Data Science

Countries: