k-匿名数据集的增量更新算法  

Algorithm for Increment Update of k-Anonymized Dataset

作　　者：宋金玲[1,2] 赵威[2] 刘欣[1] 黄立明[1] 李金才[2] 刘国华[2] 

机构地区：[1]河北科技师范学院,秦皇岛066004 [2]燕山大学信息科学与工程学院,秦皇岛066004

出　　处：《计算机科学》2010年第4期146-150,170,共6页Computer Science

基　　金：国家自然科学基金(No.60773100);河北省自然科学基金(No.F2009000475);秦皇岛市科学技术研究与发展计划项目(2008-1-12)资助

摘　　要：发布k-匿名数据集可以起到有效保护隐私的目的,但如何保持k-匿名数据集与原始数据集的同步更新是一个亟待解决的问题。为了解决这个问题,在详细分析k-匿名数据集更新情况的基础上,给出了k-匿名数据集的增量更新算法:针对具体的更新操作,首先根据语义贴近度及元组映射等方法对更新元组在k-匿名数据集中进行定位,再对更新元组进行相应的更新操作。所提算法不仅保证了数据集的k-匿名约束性质,而且保证了k-匿名数据集与原始数据集的实时一致性。K-anonymity is an effective method to prevent linking attack and protect privacy. The main idea of k-anonymity is generalizing the values on a set of special attributes named quasi-identifier, so that gains a k-anonymized dataset in which the values of each tuple on quasi-identifier must repeat at least k occurrences. Although k-anonymized dataset guarantees privacy, the k-anonymized dataset needs to be updated constantly because the original dataset updates occasionally after a version of k-anonymized dataset has been existed. So, how to update the k-anonymized dataset as well as the original dataset becomes an urgent problem. To solve this problem, based on the detailed analysis to various update situations of the k-anonymized dataset, the increment update algorithms for the k-anonymized dataset were presented. Distinguishing the specific update operation, the position of the tuple to update was located firstly by different location methods such as Semantic Similarity Degree, tuple mapping. Then the corresponding update operation was processed to the updating tuple after the location. The above update algorithms not only can ensure the k-anonymized dataset achieving k-anonymity constraints, but also can guarantee the real-time consistency between k-anonymized dataset and original dataset.

关 键 词：K-匿名 增量更新 插入 删除 修改 

分 类 号：TP309.2[自动化与计算机技术—计算机系统结构]