# A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
## 2 Related Work
### Approaches on Multi-Centered Medial Data  
In this section, we introduce the existing privacy-preserving methods from different perspectives.  
When combining data from different sources, patient privacy should be considered. [PERSON] et al. [3] proposed a solution for collecting data distributed across different departments and conducting data mining on an Internet of Health Things environment. The solution they proposed used locality-sensitive hashing (LSH) to consolidate data collected from multiple locations without concerning patient privacy. [PERSON] et al. [5] and [PERSON] et al. [6] proposed privacy-preserving record linkage methods that connect multiple databases using a hash technique. In addition, [PERSON] et al. [24] developed a platform that allows hundreds of clinical sites to share data and securely deliver the data number to external investigators. The platform uses homomorphic cryptography to provide end-to-end confidentiality and differential privacy for patient identification.  
Most machine learning techniques that are actively employed in data analysis concentrate the data in a central storage to train a model. Due to the nature of the medical data containing sensitive information, exporting the data to the outside the institutional network is subject to numerous restrictions by policy or by law. Joint techniques for the learning of artificial intelligence models in a distributed environment without exposing data outside the institution have been actively studied [25; 26; 27]. Existing learning algorithms for artificial intelligence models collect the data necessary for learning in the central storage. By contrast, federated learning places the data in the locations where they are created and periodically aggregates only the learning (intermediate) states. Because the data are not exposed to the outside, federated learning is relatively free from privacy issues. [PERSON] et al. [28] suggested a method for learning a robust decision-making model in a distributed environment, although they did not explicitly mention any federated learning in their study.  
Another obstacle is the data format. To extract the same information from data from multiple institutions, it is necessary to understand the database schema of all hospitals. Because most organizations treat database schema as confidential, not only is such an extraction extremely tedious, it is also difficult for external investigators to determine the data format of a particular hospital. To solve this problem, efforts have been made to unify the format of medical data in recent years. The common data model (CDM) is the most successful example of standardizing a medical data representation [7; 8; 9]. The data custodian of the hospital converts the original medical data of the institution into CDM data through an extract-transform-load (ETL) [11; 12]. Most CDM specifications require anonymization to remove sensitive information contained in the original data during the ETL [13; 14]. By unifying the format of such complex medical data, researchers and data analysts can explore and analyze data from multiple hospitals in the same way. In general, analysts use scripts or programs written in arbitrary programming languages. Although a CDM brings about many benefits to data analysis, many medical institutionsstill maintain their data on the premises owing to legal issues. This operation policy allows medical data to be logically or physically distributed over the network. To improve the research environment, research platforms for multi-center medical data have been constructed [29; 30].