Computer Science ›› 2018, Vol. 45 ›› Issue (6): 36-40.doi: 10.11896/j.issn.1002-137X.2018.06.006

• WISA2018 • Previous Articles     Next Articles

Mining Method of Association Rules Based on Differential Privacy

CUI Yi-hui, SONG Wei, PENG Zhi-yong, YANG Xian-di   

  1. Computer School,Wuhan University,Wuhan 430072,China
  • Received:2017-03-11 Online:2018-06-15 Published:2018-07-24

Abstract: With the advent of the era big data,the potential value of mining big data has attracted more and more attention from academia and industry.However,at the same time,due to frequent Internet security incidents,users are increasingly concerned about the disclosure of personal privacy data,and user data security issues become one of the most important obstacles to big data analysis.With regard to the study of user data security,the existing researches more focus on access control,ciphertext retrieval and result verification.The above researches can guarantee the security of user data itself,but can not dig out the potential value of protected data.Therefore,how to protect the security and dig the potential value of the data in the meantime is one of the key issues that need to be addressed.This paper proposed an association rules mining method based on differential privacy protection.Data owners use Laplacian mechanism and exponential mechanism to protect user data during data release.Data analysis is associated with differential privacy FP-tree Rule mining.The experimental results show that the performance and accuracy of the proposed method are superior to the existing methods.

Key words: Privacy preserving data mining, Differential privacy, Laplace mechanism, Exponential mechanism

CLC Number: 

  • TP311
[1]AGRAWAL R,SRIKANT R.Privacy-preserving data mining[C]//ACM Sigmod International Conference on Management of Data.ACM,2000:439-450.
[2]CHANDRAMOULI B,GOLDSTEIN J,QUAMAR A.Scalable progressive analytics on big data in the cloud[J].Proceedings of the VLDB Endowment,2013,6(14):1726-1737.
[3]CHANDRAMOULI B,GOLDSTEIN J,DUANS.Temporal analytics on big data for web advertising[C]//International Confe-rence on Data Engineering.IEEE Computer Socieyt,2013:90-101.
[4]LI B,MAZUR E,DIAO Y,et al.A platform for scalable one-pass analytics using mapreduce[C]//ACM SIGMOD International Conference on Management of Data.ACM,2011:985-996.
[5]JOHNSON A,SHMATIKOV V.Privacy-preserving data exploration in genome-wide association studies[C]//Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.ACM,2013:1079-1087.
[6]BONOMI L,XIONG L.Mining frequent patterns with differential privacy[J].Proceedings of the VLDB Endowment,2013,6(12):1422-1427.
[7]XU S,SU S,CHENG X,et al.Differentially private frequent sequence mining via sampling-based candidate pruning[C]//2015 IEEE 31st International Conference on Data Engineering (ICDE).IEEE,2015:1035-1046.
[8]LI N,QARDAJI W,SU D,et al.Privbasis:Frequent itemset mining with differential privacy[J].Proceedings of the VLDB Endowment,2012,5(11):1340-1351.
[9]ZENG C,NAUGHTON J F,CAI J Y.On differentially private frequent itemsetmining[J].Proceedings of the VLDB Endowment,2012,6(1):25-36.
[10]BHASKAR R,LAXMAN S,SMITH A,et al.Discovering frequent patterns in sensitive data[C]//Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Disco-very and Data Mining.ACM,2010:503-512.
[11]WONG K S,KIM M H.Privacy-preserving frequent itemsets mining via secure collaborative framework[J].Security and Communication Networks,2012,5(3):263-272.
[12]NANAVATI N R,JINWALA D C.A novel privacy‐preserving scheme for collaborative frequent itemset mining across vertically partitioned data[J].Security and Communication Networks,2015,8(18):4407-4420.
[13]DWORK C,ROTH A.The algorithmic foundations of differential privacy[J].Foundations and Trends in Theoretical Compu-ter Science,2014,9(3/4):211-407.
[14]DWORK C,MCSHERRY F,NISSIM K,et al.Calibrating noise to sensitivity in private data analysis[C]//Theory of Cryptography Conference.Springer Berlin Heidelberg,2006:265-284.
[15]GIANNOTTI F,LAKSHMANAN L V S,MONREALE A,et al.Privacy-preserving mining of association rules from outsourced transaction databases[J].IEEE Systems Journal,2013,7(3):385-395.
[16]MCSHERRY F D.Privacy integrated queries:an extensible platform for privacy-preserving data analysis[C]//Proceedings of the 2009 ACM SIGMOD International Conference on Management of Data.ACM,2009:19-30.
[17]ROY I,SETTY S T V,KILZER A,et al.Airavat:Security and Privacy for MapReduce[C]//Usenix Symposium on Networked Systems Design and Implementation(NSDI 2010).San Jose,CA,USA,2010:297-312.
[18]HAN J,PEI J,YIN Y.Mining frequent patterns without candidate generation[C]//ACM SIGMOD International Conference on Management of data.ACM,2000:1-12.
[19]XIONG P,ZHU T Q,WANG X F.A survey on differential privacy and applications[J].Chinese Journal of Computers,2014,37(1):101-122.(in Chinese)
[20]BLAKE C L,MERZ C J.UCI Repository of machine learning databases [OL].
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