Confidentiality preservation is of high concern in collaboration, which may involve the flow of sensitive information between collaborators. This concern is a potential barrier to forming collaborations that may otherwise enhance each collaborator’s individual contribution, and raises the need to study the trade-off between value gain and confidentiality loss from information exchange. In this paper, we analyze this trade-off by considering different revelation strategies in a collaborative design scenario. We propose a framework that provides a guideline for designers to evaluate their respective revelation strategies and thus make better decisions when choosing a particular revelation strategy in a design iteration. This framework utilizes concepts from Bayesian updating and quantifies the confidentiality lost and value gained for a particular revelation, providing a mathematical abstraction of the collaborative design process as a sequence of information revelation decisions. We illustrate the use of our proposed framework in an automobile suspension design scenario, and show the changes in performance (Alice’s and Bob’s objective function responses) and confidentiality (KL divergence) for each.