Distributed Clustering in the Anonymized Space with Local Differential Privacy

By Lin Sun, Jun Zhao, Xiaojun Ye. 2019

8 citations recorded by the source corpus.

Clustering and analyzing on collected data can improve user experiences and quality of services in big data, IoT applications. However, directly releasing original data brings potential privacy concerns, which raises challenges and opportunities for privacy-preserving clustering. In this paper, we study the problem of non-interactive clustering in distributed setting under the framework of local differential privacy. We first extend the Bit Vector, a novel anonymization mechanism to be functionality-capable and privacy-preserving. Based on the modified encoding mechanism, we propose kCluster algorithm that can be used for clustering in the anonymized space. We show the modified encoding mechanism can be easily implemented in existing clustering algorithms that only rely on distance information, such as DBSCAN. Theoretical analysis and experimental results validate the effectiveness of the proposed schemes.

Subfields: Artificial Intelligence, Computer Science Applications, Sociology and Political Science

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Citations in the corpus

The Power of the Hybrid Model for Mean Estimation (2018, methodology): “Other DP K-means algorithms exist in both the TCM [4, 8, 43, 46, 48, 53] and LM [47, 52, 54, 57] which improve on our two non-hybrid K-means algorithms.”