Fuzzy set based data publishing for privacy preservation
Mengbo Xie, Quan Qian · 2016
K-anonymity and its successors, like l-diversity and t-closeness, are the most popular approaches for privacy preserving data publishing. However, each method has relatively high information loss and computational complexity. In order to solve this problem, this paper presents a fuzzy set based anonymity algorithm, where numerical data are transformed to linguistic data and sensitive data are published in conjunction with fuzzy draft rate. The experimental results show that the fuzzy based algorithm performs better than that of the k-anonymity method from the points of information loss and execution performance. The information loss of the fuzzy based algorithm has been reduced by 40%~50% and the execution time reduced by 48%~59%.