An Improved Apriori Preserving Differential Privacy in the Framework of Spark
Zhiqiang Gao, Longjun Zhang, Renyuan Hu, Qingpeng Li, Jihua Yang · 2016
Aimed at the problem that traditional methods fail to deal with malicious attacks under arbitrary background knowledge during the process of massive data analysis, an improved Apriori algorithm preserving differential privacy, combining with Laplace mechanism to mine the pattern of sensitive information in framework of Spark is proposed.Furthermore, it's theoretically proved to meet ε-differential privacy in spark.Finally, experimental results show that guaranteeing availability, our proposed algorithm has advantages over privacy protection and satisfaction in aspects of time as well as efficiency.Most importantly, our algorithm shows a good application prospect in the analysis of data pattern mining preserving privacy protection.Also, it has better ability of privacy protection and timeliness under the premise of ensuring availability.