Improvement of Pk-Anonymization
Miho Kakizawa, Chiemi Watanabe, Ryo Furukawa, Tsubasa Takahashi · 2014
Pk-anonymization is a data anonymization method that employs randomization. Pk-anonymization guarantees Pk-anonymity, which is an extension of probabilistic k-anonymity. To implement this method, we assign random noise to records to reduce the probability of identifying record owners to less than 1/k. Existing methods assign noise using a Laplace distribution, and determine the variance of the Laplace distribution at a desired value of k to satisfy Pk-anonymity. In this paper, we propose an algorithm that improves the implementation of Pk-anonymization with smaller variance. We demonstrate the advantage of the proposed method by comparing it with an existing method.