Chinese Journal of Network and Information Security ›› 2016, Vol. 2 ›› Issue (5): 64-76.doi: 10.11959/j.issn.2096-109x.2016.00057

• Papers • Previous Articles     Next Articles

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   

  1. College of Computer Science and Engineering,Northeastern University,Shenyang 110819,China
  • Revised:2016-04-25 Online:2016-05-15 Published:2020-03-26
  • Supported by:
    The National Natural Science Foundation of China(61173030);The National Natural Science Foundation of China(61272181);The National Natural Science Foundation of China(61272182);The National Natural Science Foundation of China(61332014);The National Natural Science Foundation of China(61370154);The National Natural Science Foundation of China(61332006)

Abstract:

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.

Key words: query privacy protection,, computational private information retrieval, Spark, location based service

CLC Number: 

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