CASTLE: Continuously Anonymizing Data Streams

— 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 incoming data and the corresponding 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. Our extensive performance study shows that CASTLE is efficient and effective w.r.t. the quality of the output data.

• Type: Journal
• Year: 2011
• Where: TDSC
• Authors: Jianneng Cao, Barbara Carminati, Elena Ferrari, Kian-Lee Tan
• Keywords: Corresponding Anonymized Output, Extensive Performance Study, Static Data Sets, TDSC 2011, Theoretical Computer Science