Over the past decade, many scientific experiments have shifted from on-premise environments to the cloud. While clouds offer flexibility, scalability, and costeffectiveness, security, and confidentiality remain an issue. This is particularly true when experiments are modeled as workflows and executed using cloud-based workflow systems. These systems typically use multiple virtual machines (VMs) and shared cloud storage to execute the workflow and store the files generated during workflow execution. If these files are accessed by malicious users, they could reveal sensitive information about the workflow's results or structure. To mitigate these risks, data dispersion and techniques such as encryption can be employed, but they need to be carefully integrated into the workflow scheduling process. For example, dispersing data to storage far from the processing VM may increase workflow makespan and costs. In this manuscript, we propose CYCLOPS, an approach designed to execute workflows efficiently in clouds while addressing data confidentiality requirements. CYCLOPS incorporates a mathematical model and a Greedy Constructive Heuristic to optimize workflow scheduling. We evaluated the approach using both synthetic and real-world workflows. The results demonstrate that CYCLOPS enhances workflow execution efficiency while ensuring that data confidentiality is maintained.