Due to the rapid development of edge devices and existing infrastructure in the post-5G era, a significant flood of large and diverse data is streaming into cloud infrastructure via services on edge devices. Consequently, many cloud infrastructure providers must develop methods for efficient resource allocation and scheduling to support service deployment with high availability and reliability. However, existing infrastructure providers often overlook efficient resource provisioning to preserve data privacy from edge device services. Typically, every service employs high-security mechanisms to preserve data privacy, but this comes at the cost of high resource usage. On the other hand, employing low-security mechanisms may pose a high risk of violating data privacy. Therefore, we propose the creation of an infrastructure focused on ensuring high availability and reliability of service deployment, coupled with effective security mechanisms tailored to each individual data-driven service, all while minimizing financial costs for service developers. We propose a resource integration to categorize the transferred data from edge devices to the cloud for each service into four protection levels based on privacy requirement. We analyze patterns of data transmission based on possible privacy-violation severities and transmission frequency to prevent over or under allocation of resources, ensuring high availability, reliability, and security.