# Survey on Secure Scientific Workflow Scheduling in Cloud Environments
## 1 Introduction  
The rapid development of information technology has led to exponential growth in data generation, and the need for processor speed has increased. Therefore, the argument for a storage place capable of storing huge amounts of data increased, so the trend towards cloud services began, and this trend increased the need to maintain data security and simplify it during use. Cloud computing is an essential technology that has arisen to fulfill the growing demand for more cost-effective information technology services. It depicts a concept in which the cloud service provider provides a pool of resources, such as computing power, storage, bandwidth, and so on, in the form of on-demand services that consumers can rent over the Internet [1, 2, 3].  
In this context, many tools have been utilized to facilitate data processing, including workflow. Workflows are a typical application model in computational science. They define a series of computations that allow for structured and distributed data processing and are often stated as a set of jobs with interdependencies. These apps provide an efficient method of processing and retrieving insight from the ever-growing data generated by increasingly powerful technologies [4, 5].  
A directed acyclic graph (DAG), typically used to depict scientific workflows, is highly vulnerable to attacks since errors made in the middle will be reflected in the end product. Moreover, the fundamental knowledge in certain scientific domains is frequently hidden in the intermediate data of scientific procedures. Users will suffer enormous damages if their data are taken [6, 7, 8]. Adversaries can affect workflow execution in various ways: (i) Attackers can get inside virtual machines (VMs) running workflows and make them godown. (ii) Instead of disrupting the workflow, the adversaries may aim to tamper with its execution result by manipulating the execution software and workflow intermediate data. (iii) Once inside the virtual machines, the adversaries can also steal the workflow data or implant the backdoor to facilitate the next invasion [6; 9; 10].  
Recently, security has gained popularity as a requirement, and there are numerous ways to implement it. Either (1) separate data into sensitive and non-sensitive groups and send them to a cloud (public or private) or (2) make sure that all data processed in public clouds is safe by providing security services like authentication, integrity verification, and privacy [11]. Reducing the number of tasks in a scientific workflow is an important problem in cloud computing. Some workflow scheduling algorithms apply security services such as authentication, integrity verification, and encryption for sensitive and non-sensitive tasks. However, this approach requires a lengthy implementation period and incurs financial costs.  
This paper explores the various methods and algorithms developed to secure scientific workflow scheduling in cloud environments. We analyze the effectiveness of these approaches, focusing on the trade-offs between security, execution time, and cost. The goal is to comprehensively understand the current landscape and highlight key areas for future research in secure cloud-based scientific workflows.