Privacy-Preserving Assessment of Social Network Data Trustworthiness

Chenyun Dai, Fang-Yu Rao, Traian Marius Truta, Elisa Bertino
2014 International Journal of Cooperative Information Systems  
Extracting 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
more » ... uch 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.
doi:10.1142/s0218843014410044 fatcat:73igpjs4q5dgtbzo53vj4oj5q4