CASTLE: Continuously Anonymizing Data Streams

被引:79
作者
Cao, Jianneng [1 ]
Carminati, Barbara [2 ]
Ferrari, Elena [2 ]
Tan, Kian-Lee [1 ]
机构
[1] Natl Univ Singapore, Sch Comp, Singapore 117417, Singapore
[2] Univ Insubria, DICOM, I-22100 Varese, Italy
关键词
Data stream; privacy-preserving data mining; anonymity; K-ANONYMITY; PRIVACY;
D O I
10.1109/TDSC.2009.47
中图分类号
TP3 [计算技术、计算机技术];
学科分类号
0812 ;
摘要
Most of the existing privacy-preserving techniques, such as k-anonymity methods, are designed for static data sets. As such, they cannot be applied to streaming data which are continuous, transient, and usually unbounded. Moreover, in streaming applications, there is a need to offer strong guarantees on the maximum allowed delay between incoming data and the corresponding anonymized output. To cope with these requirements, in this paper, we present Continuously Anonymizing STreaming data via adaptive cLustEring (CASTLE), a cluster-based scheme that anonymizes data streams on-the-fly and, at the same time, ensures the freshness of the anonymized data by satisfying specified delay constraints. We further show how CASTLE can be easily extended to handle l-diversity. Our extensive performance study shows that CASTLE is efficient and effective w.r.t. the quality of the output data.
引用
收藏
页码:337 / 352
页数:16
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