Privacy-preserving clustering with distributed EM mixture modeling

Privacy and security considerations can prevent sharing of data, derailing data mining projects. Distributed knowledge discovery can alleviate this problem. We present a technique that uses EM mixture modeling to perform clustering on distributed data. This method controls data sharing, preventing disclosure of individual data items or any results that can be traced to an individual site.

[1]  Rakesh Agrawal,et al.  Privacy-preserving data mining , 2000, SIGMOD 2000.

[2]  Yehuda Lindell,et al.  Privacy Preserving Data Mining , 2000, Journal of Cryptology.

[3]  Setsuo Ohsuga,et al.  INTERNATIONAL CONFERENCE ON VERY LARGE DATA BASES , 1977 .

[4]  Gu Si-yang,et al.  Privacy preserving association rule mining in vertically partitioned data , 2006 .

[5]  Xiao-Li Meng,et al.  Using EM to Obtain Asymptotic Variance-Covariance Matrices: The SEM Algorithm , 1991 .

[6]  Silvio Micali,et al.  How to play ANY mental game , 1987, STOC.

[7]  Andrew W. Moore,et al.  Very Fast EM-Based Mixture Model Clustering Using Multiresolution Kd-Trees , 1998, NIPS.

[8]  Chris Clifton,et al.  Privacy-preserving distributed mining of association rules on horizontally partitioned data , 2004, IEEE Transactions on Knowledge and Data Engineering.

[9]  Josh Benaloh,et al.  Secret Sharing Homomorphisms: Keeping Shares of A Secret Sharing , 1986, CRYPTO.

[10]  KantarciogluMurat,et al.  Privacy-Preserving Distributed Mining of Association Rules on Horizontally Partitioned Data , 2004 .

[11]  G. McLachlan,et al.  The EM algorithm and extensions , 1996 .

[12]  Geoffrey J. McLachlan,et al.  Finite Mixture Models , 2019, Annual Review of Statistics and Its Application.

[13]  A. Raftery,et al.  Model-based Gaussian and non-Gaussian clustering , 1993 .

[14]  Charu C. Aggarwal,et al.  On the design and quantification of privacy preserving data mining algorithms , 2001, PODS.

[15]  Jaideep Vaidya,et al.  Privacy preserving association rule mining in vertically partitioned data , 2002, KDD.

[16]  D. Rubin,et al.  Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper , 1977 .

[17]  Jayant R. Haritsa,et al.  Maintaining Data Privacy in Association Rule Mining , 2002, VLDB.

[18]  D. N. Geary.  Mixture Models: Inference and Applications to Clustering , 1989 .

[19]  Chris Clifton,et al.  Privacy-preserving k-means clustering over vertically partitioned data , 2003, KDD '03.

[20]  G. Celeux,et al.  Stochastic versions of the em algorithm: an experimental study in the mixture case , 1996 .

[21]  Adrian E. Raftery,et al.  How Many Clusters? Which Clustering Method? Answers Via Model-Based Cluster Analysis , 1998, Comput. J..

[22]  A. Yao,et al.  Fair exchange with a semi-trusted third party (extended abstract) , 1997, CCS '97.