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- W2068657377 abstract "Most of 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 an incoming data and its anonymized output. To cope with these requirements, in this paper, we present CASTLE (Continuously Anonymizing STreaming data via adaptive cLustEring), 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 [1]. Our extensive performance study shows that CASTLE is efficient and effective." @default.
- W2068657377 created "2016-06-24" @default.
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- W2068657377 date "2008-04-01" @default.
- W2068657377 modified "2023-09-24" @default.
- W2068657377 title "CASTLE: A delay-constrained scheme for k<inf>s</inf>-anonymizing data streams" @default.
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- W2068657377 doi "https://doi.org/10.1109/icde.2008.4497561" @default.
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