Conference proceeding
Privacy-preserving collaborative social networks
INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS, v 5075, pp 114-125
01 Jan 2008
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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Details
- Title
- Privacy-preserving collaborative social networks
- Creators
- Justin Zhan - Carnegie Mellon Univ, Pittsburgh, PA 15213 USAGary Blosser - Carnegie Mellon UniversityChris Yang - Chinese University of Hong KongLisa Singh - Georgetown University
- Publication Details
- INTELLIGENCE AND SECURITY INFORMATICS, PROCEEDINGS, v 5075, pp 114-125
- Conference
- Pacific Asia Workshop on Intelligence and Security Informatics (Taipei, Taiwan, 17 Jun 2008–17 Jun 2008)
- Series
- Lecture Notes in Computer Science
- Publisher
- Springer Nature
- Number of pages
- 2
- Resource Type
- Conference proceeding
- Language
- English
- Academic Unit
- Information Science (Informatics)
- Web of Science ID
- WOS:000256667900013
- Scopus ID
- 2-s2.0-45849083894
- Other Identifier
- 991021861112704721
InCites Highlights
Data related to this publication, from InCites Benchmarking & Analytics tool:
- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Computer Science, Artificial Intelligence
- Computer Science, Theory & Methods