Surveillance and falsification implications for open source intelligence investigations

© 2015 ACM. Legitimacy of surveillance is crucial to safeguarding validity of OSINT data as a tool for law-enforcement agencies

Online Deception in Social Media

The unknown and the invisible exploit the unwary and the uninformed for illicit financial gain and reputation damage

Privacy-preserving Assessment of Social Network Data Trustworthiness

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 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