L-Diversity for Data Analysis: Data Swapping with Customized Clustering
Lingala Thirupathi, C. Kishor Kumar Reddy, B. V. Ramana Murthy, Rajashekar Shastry, Y.V.S.Sai Pragathi · Journal of Physics Conference Series · 2021
Abstract Data anonymization should support the analysts who intend to use the anonymized data. Releasing datasets that contain personal information requires anonymization that balances privacy concerns while preserving the utility of the data. This work shows how choosing anonymization techniques with the data analyst requirements in mind improves effectiveness quantitatively, by minimizing the discrepancy between querying the original data versus the anonymized result, and qualitatively, by simplifying the workflow for querying the data.