The known adversary threat model has drawn growing attention of the security community. The known adversary is any individual with elevated first-hand knowledge of a potential victim and/or elevated access to a potential victim’s devices. However, little attention is given on how to carefully recruit paired participants for user studies, who are qualified as legitimate known adversaries. Also, there is no formal framework for detecting and quantifying the known adversary. We develop three models, inspired by Social Psychology literature, to quantify the known adversary in paired user studies, and test them using a case study. Our results indicate that our proposed adapted-relationship closeness inventory and known adversary inventory models could accurately quantify and predict the known adversary. We subsequently discuss how social network analysis and artificial intelligence can automatically quantify the known adversary using publicly available data. We further discuss how these technologies can help the development of privacy assistants, which can automatically mitigate the risk of sharing sensitive information with potential known adversaries.