Abstract:

Privacypreserving data publishing becomes a hotspot technique in privacy preserving research, which mainly focuses on how to avoid leakage of sensitive data in data publishing, and at the meantime ensures efficient use of data. We propose a fuzzy kanonymity model for privacypreserving data publishing. We first calculate the prior probability of training sample data, and then achieve privacy protection through the Bayesian classification of a single sensitive attribute and two associated attributes. Theoretical analysis and experimental results show that compared with the classic kanonymity model, the proposal is more efficient, preserves more information, and has stronger practical applicability.

Key words: data publishing;privacy preserving;fuzzy sets;Bayesian classification;kanonymity