Please use this identifier to cite or link to this item:
|Title:||CASTLE: Continuously anonymizing data streams|
privacy-preserving data mining
|Citation:||Cao, J., Carminati, B., Ferrari, E., Tan, K.-L. (2011). CASTLE: Continuously anonymizing data streams. IEEE Transactions on Dependable and Secure Computing 8 (3) : 337-352. ScholarBank@NUS Repository. https://doi.org/10.1109/TDSC.2009.47|
|Abstract:||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. © 2011 IEEE.|
|Source Title:||IEEE Transactions on Dependable and Secure Computing|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on May 19, 2018
WEB OF SCIENCETM
checked on Apr 10, 2018
checked on May 12, 2018
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.