SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness
The VLDB Journal — The International Journal on Very Large Data Bases
User generated (web) content: trash or treasure
Proceedings of the 12th International Workshop on Principles of Software Evolution and the 7th annual ERCIM Workshop on Software Evolution
A modification of the Lloyd algorithm for k-anonymous quantization
Information Sciences: an International Journal
Fast clustering-based anonymization approaches with time constraints for data streams
Knowledge-Based Systems
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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 \ell-diversity. Our extensive performance study shows that CASTLE is efficient and effective w.r.t. the quality of the output data.