Generating t-Closed Partitions of Datasets with Multiple Sensitive Attributes
Gowda, V. T. & Bagai, R. (2023). "Generating t-Closed Partitions of Datasets with Multiple Sensitive Attributes," 7th International Conference on Cryptography, Security and Privacy (CSP), Tianjin, China, 2023, pp. 107-111, doi: 10.1109/CSP58884.2023.00024.
The popular t-closeness privacy model requires the "distance" between the distribution of sensitive attribute values in any given raw dataset and their distribution in every equivalence class created to not exceed some privacy threshold t. While most existing methods for achieving t-closeness handle data with just a single sensitive attribute, datasets with multiple sensitive attributes are very common in the real world. Here we demonstrate a technique for creating equivalence classes from a dataset containing multiple sensitive attributes. The equivalence classes generated by our method satisfy t-closeness without taking any