Monitoring security and trust in on-line personalised recommendation systems is now recognised as a key challenge. Noisy data, or maliciously biased data, can significantly skew the system's output. This paper outlines our research goals, which aim to tackle this issue along a number of lines. Game theoretic techniques are applied to determining bounds on the effect of robustness attacks on recommender systems. Graph theoretic techniques are used to analyse the dataset structure and identify influential users in the application user-group, for filtering purposes.