Workload-aware anonymization
Top Cited Papers
- 20 August 2006
- proceedings article
- Published by Association for Computing Machinery (ACM)
- p. 277-286
- https://doi.org/10.1145/1150402.1150435
Abstract
Protecting data privacy is an important problem in micro- data distribution. Anonymization algorithms typically aim to protect individual privacy, with minimal impact on the quality of the resulting data. While the bulk of previous work has measured quality through one-size-flts-all mea- sures, we argue that quality is best judged with respect to the workload for which the data will ultimately be used. This paper provides a suite of anonymization algorithms that produce an anonymous view based on a target class of workloads, consisting of one or more data mining tasks, as well as selection predicates. An extensive experimental evaluation indicates that this approach is often more efiec- tive than previous anonymization techniques.Keywords
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