  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.
