A Differential-Privacy Technique for Publishing Density-based Clustering Results

Namil Kim, Incheol Baek, Hyubjin Lee, Minsoo Kim, Yon Dohn Chung

http://doi.org/10.5626/JOK.2024.51.4.380

Clustering techniques group data with similar characteristics. Density-Based Spatial Clustering Analysis (DBSCAN) is widely used in various fields as it can detect outliers and is not affected by data distribution. However, the conventional DBSCAN method has a vulnerability where privacy-sensitive personal information in the original data can be easily exposed in the clustering results. Therefore, disclosing and distributing such data without appropriate privacy protection poses risks. This paper proposes a method to generate DBSCAN results that satisfy differential privacy. Additionally, a post-processing technique is introduced to effectively reduce noise introduced during the application of differential privacy and to process the data for future analysis. Through experiments, we observed that the proposed method enhances the utility of the data while satisfying differential privacy.