Privacy Preservation of Clusters with Distance as Sensitivity Measure
S R Sowmya, S. J. Manjunath · 2023
Preserving privacy in data mining is critical to protecting sensitive information while gaining valuable insights. Clustering algorithms using Euclidean distance measures often face privacy challenges due to the potential disclosure of sensitive information. This paper explores the application of micro aggregation as a protection method to improve privacy within clusters. The study focus on distance measure as Euclidian measure to identify sensitive data. The study evaluates the effectiveness of micro aggregation in mitigating privacy risks while maintaining clustering accuracy.