Inference Attacks in Peer-to-Peer Homogeneous Distributed Data Mining

Spontaneous formation of peer-to-peer agent-based data mining systems seems a plausible scenario in years to come. However, the emergence of peer-to-peer environments further exacerbates privacy and security concerns that arise when performing data mining tasks. We analyze potential threats to data privacy in a peer-to-peer agent-based distributed data mining scenario, and discuss inference attacks which could compromise data privacy in a peer-to-peer distributed clustering scheme known as KDEC.

[1]  E. Parzen.  On Estimation of a Probability Density Function and Mode , 1962 .

[2]  B. Silverman.  Density estimation for statistics and data analysis , 1986 .

[3]  Vassilios S. Verykios,et al.  Disclosure limitation of sensitive rules , 1999, Proceedings 1999 Workshop on Knowledge and Data Engineering Exchange (KDEX'99) (Cat. No.PR00453).

[4]  R. L. Stens,et al.  Sampling theory in Fourier and signal analysis : advanced topics , 1999 .

[5]  Matthias Klusch,et al.  Distributed Clustering Based on Sampling Local Density Estimates , 2003, IJCAI.

[6]  Elisa Bertino,et al.  Hiding Association Rules by Using Confidence and Support , 2001, Information Hiding.

[7]  Vijay V. Raghavan,et al.  A methodology for hiding knowledge in databases , 2002 .

[8]  Ramakrishnan Srikant,et al.  Privacy-preserving data mining , 2000, SIGMOD '00.

[9]  Chris Clifton,et al.  Tools for privacy preserving distributed data mining , 2002, SKDD.

[10]  Alexandre V. Evfimievski,et al.  Privacy preserving mining of association rules , 2002, Inf. Syst..

[11]  Sushil Jajodia,et al.  The inference problem: a survey , 2002, SKDD.

[12]  C. D. Kemp,et al.  Density Estimation for Statistics and Data Analysis , 1987 .

[13]  Wenliang Du,et al.  Building decision tree classifier on private data , 2002 .

[14]  Chris Clifton,et al.  Using Sample Size to Limit Exposure to Data Mining , 2000, J. Comput. Secur..

[15]  Ilker Hamzaoglu,et al.  Scalable, Distributed Data Mining - An Agent Architecture , 1997, KDD.

[16]  Salvatore J. Stolfo,et al.  JAM: Java Agents for Meta-Learning over Distributed Databases , 1997, KDD.

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

[18]  Yücel Saygin,et al.  Privacy preserving association rule mining , 2002, Proceedings Twelfth International Workshop on Research Issues in Data Engineering: Engineering E-Commerce/E-Business Systems RIDE-2EC 2002.

[19]  Philip K. Chan,et al.  Advances in Distributed and Parallel Knowledge Discovery , 2000 .

[20]  Chris Clifton,et al.  Using unknowns to prevent discovery of association rules , 2001, SGMD.

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

[22]  Stanley Robson de Medeiros Oliveira,et al.  Privacy preserving frequent itemset mining , 2002 .

[23]  Chris Clifton,et al.  Secure set intersection cardinality with application to association rule mining , 2005, J. Comput. Secur..

[24]  Qi Wang,et al.  Random-data perturbation techniques and privacy-preserving data mining , 2005, Knowledge and Information Systems.

[25]  Matthias Klusch,et al.  The role of agents in distributed data mining: issues and benefits , 2003, IEEE/WIC International Conference on Intelligent Agent Technology, 2003. IAT 2003..

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

[27]  Benny Pinkas,et al.  Cryptographic techniques for privacy-preserving data mining , 2002, SKDD.