Privacy-Preserving Collaborative Social Networks

Authors:
Justin Zhan;Gary Blosser;Chris Yang;Lisa Singh
Affiliations:
Carnegie Mellon University,;Carnegie Mellon University,;Chinese University of Hong Kong,;Georgetown University,
Venue:
PAISI, PACCF and SOCO '08 Proceedings of the IEEE ISI 2008 PAISI, PACCF, and SOCO international workshops on Intelligence and Security Informatics
Year:
2008

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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.