作　　者： (张燕平）; (凌捷）;

机构地区： 广东工业大学计算机学院,广州510006

出　　处： 《计算机科学》 2017年第8期157-161,共5页

摘　　要： 提出了一种改进的水平分布式环境下关联规则挖掘的隐私保护算法,该算法应用部分隐藏的随机化回答方法和同态加密技术,引入半可信第三方,将各站点的数据集进行扰乱和隐藏,并将数据的水平格式表示转换成垂直格式表示,通过位运算计算局部支持数,利用Paillier算法计算全局支持数。所提算法具有站点之间无须通信、支持数计算效率高、I/O操作次数少以及传输安全等优点。实验结果表明,所提算法提高了局部支持数的计算效率并减少了I/O操作次数。 An improved privacy-preserving algorithm based on homomorphic for association rules mining on horizontally partitioned environment was proposed in this paper.The algorithm utilizes the approach of randomized response with partial hiding and homomorphic encryption technology,introduces a semi-trusted third party,disruptes and hides the data sets of each site,convertes the horizontal format into vertical format,calculates the number of local support by bit operation and uses Paillier algorithm to compute the number of global support.The algorithm has some advantages,such as no need for communication between sites,high computational efficiency of support,fewer I/O operations and safe transport.Experimental results show that the algorithm can improve the computational efficiency of local support and reduce the number of I/O operations.

关 键 词： 关联规则挖掘 隐私保护 同态加密 位运算