Research Article
Security of Collaborative Data Publishing in Multiparty Communication
|
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 173 - Issue 1 |
| Published: Sep 2017 |
| Authors: Jayashri K. Bhosle, Vanja R. Chirchi |
10.5120/ijca2017915227
|
Jayashri K. Bhosle, Vanja R. Chirchi . Security of Collaborative Data Publishing in Multiparty Communication. International Journal of Computer Applications. 173, 1 (Sep 2017), 20-23. DOI=10.5120/ijca2017915227
@article{ 10.5120/ijca2017915227,
author = { Jayashri K. Bhosle,Vanja R. Chirchi },
title = { Security of Collaborative Data Publishing in Multiparty Communication },
journal = { International Journal of Computer Applications },
year = { 2017 },
volume = { 173 },
number = { 1 },
pages = { 20-23 },
doi = { 10.5120/ijca2017915227 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2017
%A Jayashri K. Bhosle
%A Vanja R. Chirchi
%T Security of Collaborative Data Publishing in Multiparty Communication%T
%J International Journal of Computer Applications
%V 173
%N 1
%P 20-23
%R 10.5120/ijca2017915227
%I Foundation of Computer Science (FCS), NY, USA
Abstract
More than one data provider collaborate to publish their data is considered here. m-privacy is a technique proposed to defend m-adversary during collaborative data publishing. M-privacy satisfies the privacy problem while publishing sensitive data. Apart from providing privacy to published data, it is also necessary to provide security between the data provider and third party/un-trusted server, to ensure this, Secure multiparty communication (SMC) protocol is used to provide secure data transfer from publisher and server. There were techniques such as k-anonymity, l-diversity, t-closeness, which were proposed to handle external attacks in data publishing, but none is published for considering internal attacks. This m-privacy is a technique, which considers internal attacks.
References
- C. Dwork, “Differential privacy: a survey of results,” in Proc. of the 5th Intl. Conf. on Theory and Applications of Models of Computation, 2008, pp. 1–19.
- B. C. M. Fung, K. Wang, R. Chen, and P. S. Yu, “Privacy-preserving data publishing: A survey of recent developments,” ACM Comput. Surv., vol. 42, pp. 14:1–14:53, June 2010.
- C. Dwork, “A firm foundation for private data analysis,” Commun. ACM, vol. 54, pp. 86–95, January 2011.
- N. Mohammed, B. C. M. Fung, P. C. K. Hung, and C. Lee, “Centralized and distributed anonymization for high-dimensional healthcare data,” ACM Transactions on Knowledge Discovery from Data (TKDD), vol. 4, no. 4, pp. 18:1–18:33, October 2010.
- W. Jiang and C. Clifton, “Privacy-preserving distributed k-anonymity,” in Data and Applications Security XIX, ser. Lecture Notes in Computer Science, 2005, vol. 3654, pp. 924–924.
- W. Jiang and C. Clifton, “A secure distributed framework for achieving k-anonymity,” VLDB J., vol. 15, no. 4, pp. 316–333, 2006.
- O. Goldreich, Foundations of Cryptography: Volume 2, Basic Applications. Cambridge University Press, 2004.
- Y. Lindell and B. Pinkas, “Secure multiparty computation for privacy preserving data mining,” The Journal of Privacy and Confidentiality, vol. 1, no. 1, pp. 59–98, 2009.
- A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam,-diversity: Privacy beyond k-anonymity,” in ICDE, 2006, p. 24.
- P. Samarati, “Protecting respondents’ identities in microdata release,” IEEE T. Knowl. Data En., vol. 13, no. 6, pp. 1010–1027, 2001.
- L. Sweeney, “k-anonymity: a model for protecting privacy,” Int. J.
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