Slicing: A New Approach for Privacy Preserving Data Publishing
IEEE Transactions on Knowledge and Data Engineering(2012)
摘要
Several anonymization techniques, such as generalization and bucketization, have been designed for privacy preserving microdata publishing. Recent work has shown that general- ization loses considerable amount of information, especially for high-dimensional data. Bucketization, on the other hand, does not prevent membership disclosure and does not apply for data that do not have a clear separation between quasi- identifying attributes and sensitive attributes. In this paper, we present a novel technique called slicing, which partitions the data both horizontally and vertically. We show that slicing preserves better data utility than gen- eralization and can be used for membership disclosure pro- tection. Another important advantage of slicing is that it can handle high-dimensional data. We show how slicing can be used for attribute disclosure protection and develop an ef- ficient algorithm for computing the sliced data that obey the ℓ-diversity requirement. Our workload experiments confirm that slicing preserves better utility than generalization and is more effective than bucketization in workloads involving the sensitive attribute. Our experiments also demonstrate that slicing can be used to prevent membership disclosure.
更多查看译文
关键词
membership disclosure,sliced data,high-dimensional data,sensitive attribute,better utility,privacy preserving data publishing,quasi-identifying attribute,membership disclosure protection,better data utility,new approach,attribute disclosure protection,anonymization technique,privacy,correlation,publishing,data privacy,data security,high dimensional data
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要