%0 Journal Article
%T An Efficient Clustering Algorithm for k-Anonymisation
%A Grigorios Loukides
%A Jian-Hua Shao
%A <br>Grigorios Loukides
%A and Jian-Hua Shao
%J 计算机科学技术学报
%D 2008
%I 
%X K-anonymisation is an approach to protecting individuals from being identified from data.Good k-anonymisations should retain data utility and preserve privacy,but few methods have considered these two conflicting requirements together. In this paper,we extend our previous work on a clustering-based method for balancing data utility and privacy protection, and propose a set of heuristics to improve its effectiveness.We introduce new clustering criteria that treat utility and privacy on equal terms and propose sampling-based techniques to optimally set up its parameters.Extensive experiments show that the extended method achieves good accuracy in query answering and is able to prevent linking attacks effectively.
%K k-anonymisation
%K data privacy
%K greedy clustering<br>计算方法
%K 数据安全
%K 保密措施
%K 计算机技术
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=8240383F08CE46C8B05036380D75B607&jid=F57FEF5FAEE544283F43708D560ABF1B&aid=3E071162ED84A148AB1033223E778462&yid=67289AFF6305E306&vid=EA389574707BDED3&iid=0B39A22176CE99FB&sid=847B14427F4BF76A&eid=974CBB04624305A1&journal_id=1000-9000&journal_name=计算机科学技术学报&referenced_num=0&reference_num=22