collaborative scoring with dishonest participants
Consider a set of players that are interested in collectively evaluating a set of objects. We develop a collaborative scoring protocol in which each player evaluates a subset of the objects, after which we can accurately predict each players' individual opinion of the remaining objects. The accuracy of the predictions is near optimal, depending on the number of objects evaluated by each player and the correlation among the players' preferences. A key novelty is the ability to tolerate malicious players. Surprisingly, the malicious players cause no (asymptotic) loss of accuracy in the predictions. In fact, our algorithm improves in both performance and accuracy over prior state-of-the-art collaborative scoring protocols that provided no robustness to malicious disruption.
fault tolerance randomized algorithms collaborative filtering recommendation systems