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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.
Privacy-preserving Pre-computation of Join Selectivity using Differential Privacy for the Proliferation of Pseudonymized Data Combination
Hyubjin Lee, Jong Seon Kim, Yon Dohn Chung
http://doi.org/10.5626/JOK.2022.49.3.250
With the enforcement of 3 data acts, pseudonymized information from various domains can be joined through certified expert agencies. Before joining all pseudonymized information, the expert agency provides a service that can compute the join selectivity in advance. However, the existing join selectivity pre-computation methods have vulnerabilities that can lead to privacy breaches. In this paper, we propose a privacy-preserving join selectivity pre-computation method that uses randomly generated one-time key values provided by the expert agency for anonymizing data through a one-way hash technique, and ensures differential privacy when pre-computing join selectivity. The proposed method ensures the anonymity of the data sent by the join requesting institutions to the expert agency and prevents privacy breaches that may occur in the previous join selectivity pre-computation methods. The experimental results showed that the proposed method provided effective join selectivity while satisfying differential privacy.
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