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Online First:2016-05,
Published:15 May 2016
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Shi-zhuo DENG, Ji-tao YAO, Bo-tao WANG, et al. PCPIR-V:parallel privacy protected algorithms for nearest neighbor query based on Spark[J]. Chinese Journal of Network and Information Security, 2016, 2(5): 64-76.
Shi-zhuo DENG, Ji-tao YAO, Bo-tao WANG, et al. PCPIR-V:parallel privacy protected algorithms for nearest neighbor query based on Spark[J]. Chinese Journal of Network and Information Security, 2016, 2(5): 64-76. DOI: 10.11959/j.issn.2096-109x.2016.00057.
摘 要:针对面向大数据的隐私保护查询效率低问题,利用CPIR保护程度高,实现了基于Spark的并行CPIR空间近邻查询隐私保护算法 PCPIR-V,提出了基于 Row 和 Bit 的并行策略,同时提出并实现了基于聚类的PCPIR-V的缓存优化技术。利用均匀分布、高斯分布和真实数据对PCPIR-V进行了测试验证,在40个核心范围内,PCPIR-V具有良好的扩展性,PCPIR-V缓存优化技术计算时间与朴素PCPIR-V时间相比,平均减少了20%。
To address the low-efficiency problem for query privacy protection on big data
parallel CPIR-V (PCPIR-V)
which had a high level of privacy protection for nearest neighbor query
was presented and implemented based on spark.Two parallel strategies for PCPIR-V
Row strategy and Bit strategy
were proposed.To avoid redundant multiplications
the repeated products were cached based on a clustering technique while computing CPIR on Spark.According to the evaluation results of PCPIR-V on three datasets
the scalablity of PCPIR-V is good until the number of core is larger than 40.The cost of PCPIR-V with the method of caching partial multiplication results is reduced by 20% averagely.
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