Abstract
A social network is the mapping and measuring of relationships and flows between individuals, groups, organizations, computers, web sites, and other information/knowledge processing entities. The nodes in the network are the people and groups, while the links show relationships or flows between the nodes. Social networks provide both a visual and a mathematical model for analyzing of relationships. While social network construction and analysis has taken place for a long time, social network analysis in the context of privacy-preservation is a relatively new area of research. In this paper, we focus on privately constructing a social network involving multiple independent parties. Because of privacy concerns, the parties cannot share their individual social network data directly. However, the parties could all benefit from the construction of a collaborative social network containing all the independent party network data. How multiple parties collaboratively construct a social network without breaching data privacy presents a challenge. The objective of this paper is to present a cryptographic approach for privately constructing collaborative social networks.
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References
Adamic, L., Adar, E.: How to search a social network. Social Networks 27(3), 187–203 (2005)
Blosser, G., Zhan, J.: Privacy-preserving social networks. In: Proceedings of IEEE International Conference on Information Security and Assurance (ISA 2008), Busan, Korea, April 24-26 (2008)
Chaum, D.: Security without identification. Communication of the ACM 28(10), 1030–1044 (1985)
Epic. Privacy and human rights an international survey of privacy laws and developments. Electronic Privacy Information Center (May 2003), http://www.epic.org
Goldreich, O.: The Foundations of Cryptography. Cambridge University Press, Cambridge (2004)
Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999)
Rivest, R., Adleman, L., Dertouzos, M.: On data banks and privacy homomorphisms. In: DeMillo, R.A., et al. (eds.) Foundations of Secure Computation, pp. 169–179. Academic Press, London (1978)
Singh, L., Zhan, J.: Measuring topological anonymity in social networks. In: Proceedings of IEEE International Conference on Granular Computing, Silicon Valley, USA, November 2-4 (2007)
Wang, D., Liau, C., Hsu, T.: Privacy protection in social network data disclosure based on granular computing. In: IEEE International Conference on Fuzzy Systems, Vancouver, BC, Canada, July 16-21 (2006)
Zhan, Z.: Privacy Preserving Collaborative Data Mining. PhD thesis, University of Ottawa (2006)
Zhou, B., Pei, J.: Preserving privacy in social networks against neighborhood attacks. In: Proceedings of the 24th International Conference on Data Engineering (ICDE 2008), Cancún, México, April 7-12 (2008)
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Zhan, J., Blosser, G., Yang, C., Singh, L. (2008). Privacy-Preserving Collaborative Social Networks. In: Yang, C.C., et al. Intelligence and Security Informatics. ISI 2008. Lecture Notes in Computer Science, vol 5075. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69304-8_13
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DOI: https://doi.org/10.1007/978-3-540-69304-8_13
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69136-5
Online ISBN: 978-3-540-69304-8
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