Privacy-preserving assessment of social network data trustworthiness
Published in:
Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom), 2012 8th International Conference on
Date of Conference:
14-17 Oct. 2012
Abstract
Download CitationEmailPrintRequest PermissionsExtracting useful knowledge from social network datasets is a challenging problem. To add to the difficulty of this problem, privacy concerns that exist for many social network datasets have restricted the ability to analyze these networks and consequently to maximize the knowledge that can be extracted from them. This paper addresses this issue by introducing the problem of data trustworthiness in social networks when repositories of anonymized social networks exist that can be used to assess such trustworthiness. Three trust score computation models (absolute, relative, and weighted) that can be instantiated for specific anonymization models are defined and algorithms to calculate these trust scores are developed. Using both real and synthetic social networks, the usefulness of the trust score computation is validated through a series of experiments.