Hierarchical anonymization algorithms against background knowledge attack in data releasing
作者：
Highlights：
• We define a privacy model based on k-anonymity and one of its strong refinements to prevent the background knowledge attack.
• We propose two hierarchical anonymization algorithm to satisfy our privacy model.
• Our algorithms outperform the state-of the art anonymization algorithm in terms of utility and privacy.
• We extend an information loss measure to capture data inaccuracies caused by not-fitted records in any equivalence class.
摘要
•We define a privacy model based on k-anonymity and one of its strong refinements to prevent the background knowledge attack.•We propose two hierarchical anonymization algorithm to satisfy our privacy model.•Our algorithms outperform the state-of the art anonymization algorithm in terms of utility and privacy.•We extend an information loss measure to capture data inaccuracies caused by not-fitted records in any equivalence class.
论文关键词：Privacy preservation,Tabular data,Hierarchical anonymization algorithm,Background knowledge,Information loss metric
论文评审过程：Received 15 August 2015, Revised 5 March 2016, Accepted 7 March 2016, Available online 12 March 2016, Version of Record 16 April 2016.
论文官网地址：https://doi.org/10.1016/j.knosys.2016.03.004
