Flexible Partitioning of Geographical Information based on GPS Coordinates for k-Anonymity
Yuxin Liu, Kazuhiro Minami · 2022 IEEE International Conference on Big Data (Big Data) · 2022
To release microdata of medical information for secondary use, it is necessary to anonymize it to protect the individual’s privacy in that data. In k-anonymity, we generalize identifying attributes of each individual to partition the data into groups of more than k records that take the same values for those identifying attributes. However, generalizing geographical information on individuals based on a domain- level hierarchy leads to anonymized data of low data utility because the population densities of regions vary significantly in Japan. Therefore, we develop a new technique of recursively partitioning regional information based on GPS coordinates. Our experimental results show that the proposed method adjusts the granularity of geographical information flexibly such that the resulting groups of records for k-anonymity possess much better uniformity in size than those with the conventional generalization method.