隐私问题受到越来越多的关注,基于计算的私有信息检索(CPIR)的隐私保护技术允许用户从服务提供商检索数据并且不会泄露查询信息.但是,对于大规模应用,隐私保护技术与可用性之间存在较大差距.针对CPIR算法计算量大、计算时间长而不适合应用于大规模数据隐私保护的问题,提出了基于Spark和Huff man编码的CPIR最近邻查询隐私保护算法(H-PCPIR-V).H-PCPIR-V算法主要是在数据预处理阶段将最近邻矩阵使用Huff man编码进行压缩减少计算位数,然后通过压缩后矩阵中元素的最大位数对其他元素进行补位,在服务端使用Spark并行框架对查询网格进行并行计算.通过对比实验及实验结果分析发现,相比PCPIR-V算法,H-PCPIR-V算法在服务端的计算代价下降30％左右,客户端的计算代价下降10％左右,通信代价下降40％左右.%With the privacy issues drawing more and more concerns,privacy protection techniques based on Computational Private Information Retrieval (CPIR) allow a user to retrieve data from a service provider without revealing the users query information.For large-scale applications,there exists a gap between privacy protection techniques and its feasibility.For the problem that the CPIR algorithm needs long computing time so as not to be suitable for large-scale data privacy protection,this paper proposes a CPIR nearest neighbor privacy protection algorithm (H-PCPIR-V) based on Spark and Huffman code.The H-PCPIR-V algorithm partitions the spatial data into Voronoi diagrams according to the points of interest in the data preprocessing stage,and then utilizes the Huffman code to compress the candidate data in order to reduce bit computation operation.Spark parallel framework is used for query grid parallel computing in the server side.The experimental results show that the computational cost of HPCPIR-V algorithm is about 30 ％ lower than that of PCPIR-V algorithm on the server side,the computational cost of client is about 10％ lower,and the communication cost is about 40％ lower.
展开▼
