Journal Article

CASTLE: Continuously Anonymizing Data Streams

Kian-Lee Tan, Carminati, B, Jianneng Cao, Ferrari, E

2011

Overview

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.

• Publisher: IEEE,IEEE Computer Society
• Subject: Alliances / Analysis / Anonymity / Clustering / Clusters / Computation / Continuous casting / Customers / Data analysis / Data mining / Data privacy / Data stream / Datasets / Delay / Delay effects / Design methodology / DICOM / Digital Object Identifier / Handles / Joining processes / Marketing and sales / Monitoring / Network security / Privacy / privacy-preserving data mining / Protection / Streams / Studies / Third party / Transaction databases