k-Anonymized Data by Fuzzy k-Member Clustering"> k-anonymity, fuzzy clustering, collaborative filtering"> k-Anonymized Data by Fuzzy k-Member Clustering | Keywords: k-anonymity, fuzzy clustering, collaborative filtering | Author: Arina Kawano, Katsuhiro Honda, Akira Notsu, and Hidetomo Ichihashi" /> JACIII Vol.18 p.239 (2014) | Fuji Technology Press: academic journal publisher
Nothing Special   »   [go: up one dir, main page]
Top > JACIII > Performance Comparison of Collaborative Filtering with k-Ano ...

single-jc.php

JACIII Vol.18 No.2 pp. 239-245
doi: 10.20965/jaciii.2014.p0239
(2014)

Paper:

Views over last 60 days: 580

Performance Comparison of Collaborative Filtering with k-Anonymized Data by Fuzzy k-Member Clustering

Arina Kawano*, Katsuhiro Honda*, Akira Notsu*,
and Hidetomo Ichihashi**

*Graduate School of Engineering, Osaka Prefecture University, 1-1 Gakuen-cho, Nakaku, Sakai, Osaka 599-8531, Japan

**Department of Economics, Osaka University of Economics and Law, 6-10 Gakuonji, Yao, Osaka 581-8511, Japan

Received:
October 16, 2013
Accepted:
January 31, 2014
Published:
March 20, 2014
Creative Commons License
Keywords:
k-anonymity, fuzzy clustering, collaborative filtering
Abstract
In order to perform collaborative filtering with published databases in a privacy preserving manner, databases must be anonymized beforehand. This paper studies the applicability of fuzzy k-member clustering in privacy preserving collaborative filtering with k-anonymized data, in which users’ historical data of k or more users are suppressed considering soft data partitions. By allowing boundary samples to be shared by multiple clusters, data anonymization is performed without significant loss of information. Its performances are compared with several different types of fuzzy membership functions.
Cite this article as:
A. Kawano, K. Honda, A. Notsu, and H. Ichihashi, “Performance Comparison of Collaborative Filtering with k-Anonymized Data by Fuzzy k-Member Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.18 No.2, pp. 239-245, 2014.
Data files:
References
  1. [1] J. A. Konstan, B. N. Miller, D. Maltz, J. L. Herlocker, L. R. Gardon, and J. Riedl, “Grouplens: applying collaborative filtering to usenet news,” Communications of the ACM, Vol.40, No.3, pp. 77-87, 1997.
  2. [2] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An algorithmic framework for performing collaborative filtering,” Proc. of Conf. on Research and Development in Information Retrieval, 1999.
  3. [3] G. Linden, B. Smith, and J. York, “Amazon.com recommendations: item-to-item collaborative filtering,” IEEE Internet Computing, Jan-Feb, pp. 76-80, 2003.
  4. [4] C. C. Aggarwal and P. S. Yu, “Privacy-Preserving Data Mining: Models and Algorithms,” Springer-Verlag, New York, 2008.
  5. [5] P. Samarati, “Protecting respondents’ identities in microdata release,” IEEE Trans. Knowledge and Data Engineering, Vol.13, No.6, pp. 1010-1027, 2001.
  6. [6] L. Sweeney, “k-anonymity: a model for protecting privacy,” Int. J. on Uncertainty, Fuzziness and Knowledge-based Systems, Vol.10, No.5, pp. 557-570, 2002.
  7. [7] J. W. Byun, A. Kamra, E. Bertino, and N. Li, “Efficient k-anonymization using clustering techniques,” Int. Conf. on Database Systems for Advanced Applications, LNCS-4443, pp. 188-200, Springer, 2007.
  8. [8] K. Honda, A. Kawano, A. Notsu, and H. Ichihashi, “A fuzzy variant of k-member clustering for collaborative filtering with data anonymization,” Proc. of 2012 IEEE Int. Conf. Fuzzy Systems, pp. 121-126, 2012.
  9. [9] A. Kawano, K. Honda, A. Notsu, and H. Ichihashi, “Comparison on membership functions in fuzzy k-member clustering for data anonymization,” Proc. 6th Int. Conf. on Soft Computing and Intelligent Systems and 13th Int. Symp. on Advanced Intelligent Systems, pp. 2004-2008, 2012.
  10. [10] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms, Plenum Press, 1981.
  11. [11] K. Honda, A. Notsu, and H. Ichihashi, “Collaborative filtering by sequential user-item co-cluster extraction from rectangular relational data,” Int. J. of Knowledge Engineering and Soft Data Paradigms, Vol. 2, No.4, pp. 312-327, 2010.
  12. [12] J. A. Swets, “Measuring the accuracy of diagnostic systems,” Science, Vol.240, No.4857, pp. 1285-1289, 1988.
  13. [13] K. Honda, M. Muranishi, A. Notsu, and H. Ichihashi, “FCM-type cluster validation in fuzzy co-clustering and collaborative filtering applicability,” Int. J. of Computer Science and Network Security, Vol.13, No.1, pp. 24-29, 2013.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Nov. 19, 2024