Distributed Clustering in the Anonymized Space with Local Differential Privacy

Understand

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.