广东工业大学学报2012，Vol.29Issue(3)：28-34,45,8.DOI:10.3969/j.issn.1007-7162.2012.03.005
面向聚类挖掘的局部旋转扰动隐私保护算法
Partial Rotation Perturbation for Privacy-Preserving Clustering Mining
摘要
Abstract
Many potential and valuable rules can be derived from data via clustering mining in an effective and accurate way, which can lead to security threats such as the disclosure of user privacy. Many privacy-preserving researches on clustering mining have been conducted, especially on multiplicative perturbation (MP) that is a highly secure and accurate method. Research finds known knowledge independent component analysis (KK-ICA) can greatly reduce the privacy security of existing MP. It can approximately estimate private data from MP data. To solve the problem, partial rotation perturbation (PRP) is proposed. The analysis of accuracy shows that PRP has zero-loss accuracy. The analysis of security proves that PRP can defend attack from the KK-ICA availably and is more secure. PRP is applied in clustering mining. The results are very similar to unpreserved clustering mining results, which shows that MP is practicable. The existence of PRP solves the problem with the security vulnerability of existing MP effectively, making the application of clustering more secure.关键词
聚类挖掘/隐私保护/乘法扰动/局部旋转扰动Key words
clustering mining/ privacy-preserving/ multiplicative perturbation/ partial rotation perturbation分类
信息技术与安全科学引用本文复制引用
刘洪伟,石雅强,梁周扬,肖岳..面向聚类挖掘的局部旋转扰动隐私保护算法[J].广东工业大学学报,2012,29(3):28-34,45,8.基金项目
国家自然科学基金资助项目(70971027) （70971027）
