Abstract: A system and method for data anonymization using hierarchical data clustering and perturbation is provided. The system includes a computer system and an anonymization program executed by the computer system. The system converts the data of a high-dimensional dataset to a normalized vector space and applies clustering and perturbation techniques to anonymize the data. The conversion results in each record of the dataset being converted into a normalized vector that can be compared to other vectors. The vectors are divided into disjointed, small-sized clusters using hierarchical clustering processes. Multi-level clustering can be performed using suitable algorithms at different clustering levels. The records within each cluster are then perturbed such that the statistical properties of the clusters remain unchanged.

Abstract: A system and method for data anonymization using hierarchical data clustering and perturbation is provided. The system includes a computer system and an anonymization program executed by the computer system. The system converts the data of a high-dimensional dataset to a normalized vector space and applies clustering and perturbation techniques to anonymize the data. The conversion results in each record of the dataset being converted into a normalized vector that can be compared to other vectors. The vectors are divided into disjointed, small-sized clusters using hierarchical clustering processes. Multi-level clustering can be performed using suitable algorithms at different clustering levels. The records within each cluster are then perturbed such that the statistical properties of the clusters remain unchanged.