Protecting Privacy by Multi-Dimensional K-Anonymity

Provided by: Chongqing University
Topic: Security
Format: PDF
Privacy protection for incremental data has a great effect on data availability and practicality. K-anonymity is an important approach to protect data privacy in data publishing scenario. However, it is a NP-hard problem for optimal k-anonymity on dataset with multiple attributes. Most partitions in k-anonymity at present are single-dimensional. Now research on k-anonymity mainly focuses on getting high quality anonymity while reducing the time complexity, and new method of realization of k-anonymity properties according to the requirement of published data.

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