A data anonymous method based on overlapping slicing
Jing Yang, Ziyun Liu, Yue Yang, Jianpei Zhang · 2014
In recent years, data dissemination privacy protection issues received extensive attention. A variety of privacy preserving anonymity models and technology, such as generalization, anatomy and slicing have been proposed. We present a new technique - overlapping slicing, it handles data attributes mainly based on the idea of fuzzy clustering. And we present a linear algorithm of processing data with group to generate multiple data tables, and make them satisfy l-diversity. We conduct several experiments to confirm that overlapping slicing technology ensures data security and improves the effectiveness of anonymous data at the same time. What's more, overlapping slicing processes high-dimensional data effectively.