PCPIR-V:parallel privacy protected algorithms for nearest neighbor query based on Spark - Shi-zhuo DENG; Ji-tao YAO; Bo-tao WANG; Yue-mei CHEN; Ye YUAN; Yan-hui LI; Guo-ren WANG (网络与信息安全学报)
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.