Protecting data: a fuzzy approach

References

• B. Balasko, J. Abonyi, and B. Feil, Fuzzy clustering and data analysis toolbox (for use with Matlab). Available at http://www.sunfinedata.com/wp-content/uploads/2009/10/FuzzyClusteringToolbox.pdf.
   Google Scholar
• R. Chen, B.C.M. Fung, N. Mohammed, B.C. Desai, and K. Wang, Privacy-preserving trajectory data publishing by local suppression, Inform. Sci. 231 (2013), pp. 83–97. doi: 10.1016/j.ins.2011.07.035
   Web of Science ®Google Scholar
• V. Ciriani, S. De Capitani di Vimercati, S. Foresti, and P. Samarati, Microdata protection, in Secure Data Management in Decentralized Systems, Yu, Ting and Jajodia, Sushileds., Springer, New York, 2007, pp. 291–321.
   Google Scholar
• I. Couso, S. Montes, and P. Gil, The necessity of the strong α-cuts of a fuzzy set, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 9(2) (2001), pp. 249–262. doi: 10.1142/S0218488501000788
   Web of Science ®Google Scholar
• I. Díaz, J. Ranilla, L.J. Rodríguez-Muñiz, and L. Troiano, Identifying the risk of attribute disclosure by mining fuzzy rules, in Information Processing and Management of Uncertainty in Knowledge-Based Systems. Theory and Methods, E. Hüllermeier, R. Kruse, and F. Hoffmann, eds., Springer, Berlin, 2010, pp. 455–464.
   Google Scholar
• I. Díaz, L.J. Rodríguez-Muñiz, and L. Troiano, Fuzzy sets in data protection: Strategies and cardinalities, Log. J. IGPL 20(4) (2011), pp. 657–666. doi: 10.1093/jigpal/jzr014
   Web of Science ®Google Scholar
• M. Grabisch, J.L. Marichal, R. Mesiar, and E. Pap, Aggregation Functions, Cambridge University Press, Cambridge, 2009.
   Google Scholar
• F. Hoppner, R. Klawonn, and T. Runkler, Fuzzy Cluster Analysis, John Wiley and Sons, Sussex, 1999.
   Google Scholar
• T. Li and N. Li, Towards optimal k-anonymization, Data Knowl. Eng. 65(1) (2008), pp. 22–39. doi: 10.1016/j.datak.2007.06.015
   Web of Science ®Google Scholar
• N. Li, T. Li, and S. Venkatasubramanian, t-closeness: Privacy beyond k-anonymity and l-diversity, Proceedings of the IEEE ICDE 2007, Los Alamitos, California, pp. 106–115.
   Google Scholar
• A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, l-diversity: Privacy beyond k-anonymity, ACM Trans. Knowl. Discov. Data 1(1) (2007), Article 3, ppp. 3.1–3.12. doi: 10.1145/1217299.1217302
   Google Scholar
• N. Matatov, L. Rokach, and O. Maimon, Privacy-preserving data mining: A feature set partitioning approach, Inform. Sci. 180(14) (2010), pp. 2696–2720. doi: 10.1016/j.ins.2010.03.011
   Web of Science ®Google Scholar
• Matlab Documentation Center, k-means clustering. Available at http://www.mathworks.es/es/help/stats/kmeans.html.
   Google Scholar
• S. Montes, Fuzzy δ-ε-partitions, Inform. Sci. 152 (2003), pp. 267–285. doi: 10.1016/S0020-0255(03)00055-0
   Web of Science ®Google Scholar
• A. Ralescu, A note on rule representation in expert systems, Inform. Sci. 38(2) (1986), pp. 193–203. doi: 10.1016/0020-0255(86)90020-4
   Web of Science ®Google Scholar
• E. Ruspini, A new approach to clustering, Inf. Control 15 (1969), pp. 22–32. doi: 10.1016/S0019-9958(69)90591-9
   Google Scholar
• P. Samarati, Protecting respondents’ identities in microdata release, IEEE Trans. Knowl. Data Eng. 13(6) (2001), pp. 1010–1027. doi: 10.1109/69.971193
   Web of Science ®Google Scholar
• L. Sweeney, Achieving k-anonymity privacy protection using generalization and suppression, Internat. J. Uncertain. Fuzziness Knowledge-Based Systems 10(5) (2002), pp. 571–588. doi: 10.1142/S021848850200165X
   Web of Science ®Google Scholar
• M. Templ, A. Kowarik, and B. Meindl, Statistical disclosure control methods for the generation of public- and scientific-use files. Available at http://cran.r-project.org/web/packages/sdcMicro/sdcMicro.pdf.
   Google Scholar
• L. Troiano, L.J. Rodríguez-Muñiz, J. Ranilla, and I. Díaz, Interpretability of fuzzy association rules as means of discovering threats to privacy, Int. J. Comput. Math. 89(3) (2012), pp. 325–333. doi: 10.1080/00207160.2011.613460
   Web of Science ®Google Scholar
• S. Zhong, Privacy-preserving algorithms for distributed mining of frequent itemsets, Inform. Sci. 177(2) (2007), pp. 490–503. doi: 10.1016/j.ins.2006.08.010
   Web of Science ®Google Scholar