On some clustering approaches for graphs

In this paper we discuss some tools for graph perturbation with applications to data privacy. We present and analyse two different approaches. One is based on matrix decomposition and the other on graph partitioning. We discuss these methods and show that they belong to two traditions in data protection: noise addition/microaggregation and k-anonymity.

[1]  Siddharth Srivastava,et al.  Anonymizing Social Networks , 2007 .

[2]  Kiyohiko Uehara,et al.  Fuzzy inference based on families of α-level sets , 1993, IEEE Trans. Fuzzy Syst..

[3]  Latanya Sweeney,et al.  k-Anonymity: A Model for Protecting Privacy , 2002, Int. J. Uncertain. Fuzziness Knowl. Based Syst..

[4]  László T. Kóczy,et al.  Function approximation capability of a novel fuzzy flip-flop based neural network , 2009, 2009 International Joint Conference on Neural Networks.

[5]  John A. Bernard,et al.  Use of a rule-based system for process control , 1988 .

[6]  Thomas A. Runkler,et al.  Selection of appropriate defuzzification methods using application specific properties , 1997, IEEE Trans. Fuzzy Syst..

[7]  K. Liu,et al.  Towards identity anonymization on graphs , 2008, SIGMOD Conference.

[8]  George Karypis,et al.  Multilevel k-way Partitioning Scheme for Irregular Graphs , 1998, J. Parallel Distributed Comput..

[9]  George Bojadziev,et al.  Fuzzy Sets, Fuzzy Logic, Applications , 1996, Advances in Fuzzy Systems - Applications and Theory.

[10]  Willem H. Haemers,et al.  Spectral Characterizations of Some Distance-Regular Graphs , 2002 .

[11]  Javier Herranz,et al.  Rethinking rank swapping to decrease disclosure risk , 2008, Data Knowl. Eng..

[12]  M. J. Wierman,et al.  Empirical study of defuzzification , 2003, 22nd International Conference of the North American Fuzzy Information Processing Society, NAFIPS 2003.

[13]  Enrique H. Ruspini,et al.  REACTIVE FUZZY CONTROL OF AUTONOMOUS ROBOTS , 2011 .

[14]  Josep Domingo-Ferrer,et al.  Practical Data-Oriented Microaggregation for Statistical Disclosure Control , 2002, IEEE Trans. Knowl. Data Eng..

[15]  Alessandro Gabrielli,et al.  Design of very high speed CMOS fuzzy processors for applications in high energy physics experiments , 1999, Proceedings of the Seventh International Conference on Microelectronics for Neural, Fuzzy and Bio-Inspired Systems.

[16]  Pierangela Samarati,et al.  Protecting privacy when disclosing information: k-anonymity and its enforcement through generalization and suppression , 1998 .

[17]  U. Rovira,et al.  Chapter 6 A Quantitative Comparison of Disclosure Control Methods for Microdata , 2001 .

[18]  Pierangela Samarati,et al.  Protecting Respondents' Identities in Microdata Release , 2001, IEEE Trans. Knowl. Data Eng..

[19]  Ildar Z. Batyrshin,et al.  Center of Slice Area Average Defuzzifier for Digital Implementations of Fuzzy Systems , 2010, IC-AI.

[20]  Sied Mehdi Fakhraie,et al.  Cost-Performance Co-Analysis in VLSI Implementation of Existing and New Defuzzification Methods , 2005, International Conference on Computational Intelligence for Modelling, Control and Automation and International Conference on Intelligent Agents, Web Technologies and Internet Commerce (CIMCA-IAWTIC'06).

[21]  William E. Winkler,et al.  Disclosure Risk Assessment in Perturbative Microdata Protection , 2002, Inference Control in Statistical Databases.

[22]  Vicenç Torra,et al.  Data Privacy for Simply Anonymized Social Network Logs Represented as Graphs - Considerations for Graph alteration Operations , 2011, Int. J. Uncertain. Fuzziness Knowl. Based Syst..