Continuous privacy preserving publishing of data streams
Proceedings of the 12th International Conference on Extending Database Technology: Advances in Database Technology
Relationships and data sanitization: a study in scarlet
Proceedings of the 2010 workshop on New security paradigms
SABRE: a Sensitive Attribute Bucketization and REdistribution framework for t-closeness
The VLDB Journal — The International Journal on Very Large Data Bases
FAANST: fast anonymizing algorithm for numerical streaming data
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
Disclosure detection over data streams in database publishing
Proceedings of the 2011 Joint EDBT/ICDT Ph.D. Workshop
| Hi-index | 0.00 |
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.