Secure clustering in private networks

Proceedings - IEEE International Conference on Data Mining, ICDM

Volumn , Issue , 2011, Pages 894-903

  Yang, Bin ^a   Sato, Issei ^a   Nakagawa, Hiroshi ^a  

^a UNIVERSITY OF TOKYO   (Japan)

Author keywords

Clustering; Gibbs sampling; Mixed network; Peer to peer; Privacy

Indexed keywords

CLUSTERING; CLUSTERING METHODS; COMPLEX STRUCTURE; GIBBS SAMPLING; MAXIMUM DEGREE; MIXED NETWORK; MIXED STRUCTURE; NUMBER OF CLUSTERS; PEER TO PEER; PRIVACY PRESERVING; PRIVATE INFORMATION; PRIVATE NETWORKS; RUNNING COST; SOCIAL NETWORKS;

DATA MINING; DATA PRIVACY; DISTRIBUTED COMPUTER SYSTEMS;

PEER TO PEER NETWORKS;

EID: 84857187899     PISSN: 15504786     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICDM.2011.127     Document Type: Conference Paper

References (20)

1. I. Damgard and M. Jurik. A Generalisation, a Simplification and Some Applications of Pailliers Probabilistic Public-Key System, In Public Key Cryptography 2001. Springer, 2001.
2. C. Dwork, F. McSherry, K. Nissim, and A. Smith. Calibrating noise to sensitivity in private data analysis, In Third Theory of Cryptography Conference (TCC), 2006.
3. B. Goethals, S. Laur, H. Lipmaa, and T. Mielikainen, On private scalar product computation for privacy-preserving data mining, The 7th Annual International Conference in Information Security and Cryptology, Seoul, Korea, 2004, pp. 104-120.
4. O. Goldreich, Foundations of Cryptography: Volume 2, Basic Applications., Cambridge University Press, Cambridge, 2004.
5. M. Hay, C. Li, G. Miklau, and D. Jensen, Accurate estimation of the degree distribution of private networks, The 9th IEEE International Conference on Data Mining, 2009.
6. G. Jagannathan and R. N. Wright, Privacy-preserving distributed k-means clustering over arbitrarily partitioned data, The 11th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2003.
7. S. Jha, L. Kruger, and P. McDamiel, Privacy preserving clustering, The 10th European Symposium on Research in Computer Security, 2005.
8. C. Kemp, J. B. Tenenbaum, T. L. Griffiths, T. Yamada, and N. Ueda, Learning systems of concepts with an infinite relational model, The 21st National Conference on Artificial Intelligence, 2006.
9. D. Kempe and F. McSherry, A decentralized algorithm for spectral analysis, ACM Symposium on Theory of Computing, 2004.
10. V. Krebs, http://www.orgnet.com/.
11. X. Lin, C. Clifton, and M. Zhu, Privacy-preserving clustering with distributed EM mixture, Knowledge and information systems, 2004, pp. 68-81.
12. K. Liu and E. Terzi, A framework for computing the privacy scores of users in online social networks, The 9th IEEE International Conference on Data Mining, 2009.
13. D. Malkhi, N. Nisan, B. Pinkas, and Y. Sella, Fairplay: a secure two-party computation system, USENIX Security Symposium, 2004. pp. 287-302.
14. M. E. J. Newman and E. A. Leicht, Mixture models and exploratory analysis in networks, PNAS, 2007.
15. P. Paillier, Public-Key Cryptosystems based on Composite Degree Residue Classes, Proceedings of EuroCrypt '99, 1999.
16. J. Sakuma and S. Kobayashi, Link analysis for private weighted graphs, The 32nd ACM SIGIR Conference, 2009.
17. J. Vaidya and C. Clifton, Privacy-Preserving k-means clustering over vertically partitioned data, The 9th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2003.
18. V. G. Vizing, On an estimate of the chromatic class of a p-graph, Diskret. Analiz. 3: 25-30, 1964.
19. B. Yang, H. Nakagawa, I. Sato, and J. Sakuma Collusionresistant privacy-preserving data mining, ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2010.
20. A. C. Yao, How to generate and exchange secrets, IEEE Symposium on Foundations of Computer Science, 1986.