Clouds provide an on-demand environment that allows scientists to migrate their local experiments to an elastic environment. Experiments are modeled as scientific workflows, and many of them are computing and data-intensive. The storage of these data is a concern, as confidentiality can be compromised. Malicious users may infer knowledge of the results and structure of workflows. Data dispersion and encryption can be adopted to increase confidentiality, but these mechanisms cannot be adopted uncoupled from workflow scheduling, at the risk of increasing execution time and financial costs. In this paper, we present SaFER (workflow Scheduling with conFidEntity pRoblem), a scheduling approach that considers data confidentiality constraints. * Os autores agradecem ao CNPq, CAPES a FAPERJ por financiarem parcialmente esse trabalho.