Mathematically optimized, recursive prepartitioning strategies for k-anonymous microaggregation of large-scale datasets
作者：
Highlights：
• We reduce the running time of k-anonymous microaggregation of large-scale datasets.
• A novel, mathematically optimized strategy for prepartitioning the dataset.
• Our approach owes to the superadditive running time of microaggregation.
• Running time and information loss are assessed over multiple datasets.
摘要
•We reduce the running time of k-anonymous microaggregation of large-scale datasets.•A novel, mathematically optimized strategy for prepartitioning the dataset.•Our approach owes to the superadditive running time of microaggregation.•Running time and information loss are assessed over multiple datasets.
论文关键词：Data privacy,Statistical disclosure control,k-anonymity,Microaggregation,Optimized prepartitioning,Large-scale datasets
论文评审过程：Received 21 May 2019, Revised 28 August 2019, Accepted 10 November 2019, Available online 11 November 2019, Version of Record 18 November 2019.
论文官网地址：https://doi.org/10.1016/j.eswa.2019.113086
