Efficient secure data publishing algorithms for supporting information sharing  

作　　者：YANG XiaoChun 

机构地区：[1]Key Laboratory of Medical Image Computing (Northeastern University), Ministry of Education, Shenyang 110004, China [2]School of Information Science and Engineering, Northeastern University, Shenyang 110004, China

出　　处：《Science in China(Series F)》2009年第4期627-644,共18页中国科学（F辑英文版）

基　　金：Supported by the Program for New Century Excellent Talents in Universities (Grant No. NCET-06-0290);the National Natural Science Foundation of China (Grant Nos. 60828004, 60503036);the Fok Ying Tong Education Foundation Award (Grant No. 104027)

摘　　要：Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals, such as diseases of patients, the credit rating of a customer, and the salary of an employee. Meanwhile, certain information is required to be published. In this paper, we consider data-publishing applications where the publisher specifies both sensitive information and shared information. An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data. The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data. We formulate the inference attack framework, and develop complexity results. We show that computing a safe partial table is an NP-hard problem. We classify the general problem into subcases based on the requirements of publishing information, and propose algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data. The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.Many data sharing applications require that publishing data should protect sensitive information pertaining to individuals, such as diseases of patients, the credit rating of a customer, and the salary of an employee. Meanwhile, certain information is required to be published. In this paper, we consider data-publishing applications where the publisher specifies both sensitive information and shared information. An adversary can infer the real value of a sensitive entry with a high confidence by using publishing data. The goal is to protect sensitive information in the presence of data inference using derived association rules on publishing data. We formulate the inference attack framework, and develop complexity results. We show that computing a safe partial table is an NP-hard problem. We classify the general problem into subcases based on the requirements of publishing information, and propose algorithms for finding a safe partial table to publish. We have conducted an empirical study to evaluate these algorithms on real data. The test results show that the proposed algorithms can produce approximate maximal published data and improve the performance of existing algorithms.

关 键 词：Information sharing data publishing data privacy association rule inference attack 

分 类 号：TP393[自动化与计算机技术—计算机应用技术] TD451.07[自动化与计算机技术—计算机科学与技术]