The digital transformation of health processes and the resulting availability of vast amounts of health- related data about patients offer significant promise to advance multiple medical research projects and enhance both the public and private healthcare systems. Exploiting the full potential of this vision requires a unified representation of different autonomous data sources to facilitate detailed data analysis capacity. To this end, OMOP CDM has emerged as the de facto standard for organizing healthcare data from diverse sources. However, collecting and processing sensitive data about individuals leads to consideration of privacy requirements and confidentiality concerns. Privacy-Preserving Data Integration (PPDI) is the process of establishing a unified view of personal data across multiple data sources while protecting the privacy of individuals represented in the underlying data. This discussion paper offers a concise overview of the research field related to PPDI, highlighting associated challenges and opportunities within the healthcare domain. In particular, it delves into the specific research challenges encountered by the PPDI process alongside the utilization of OMOP-CDM, with particular attention directed towards the Schema Alignment phase and the classification of data based on identifiability and privacy.