# A Secure CDM-Based Data Analysis Platform (SCAP) in Multi-Centered Distributed Setting
## 4 Results
### Implementation for CS
21 returnSUCCESS
22 else
23 returnERROR
24
25 end if
26
27 end if
```

**Algorithm 9**The AC distributing algorithm on the SS side

## 5 Discussion

### Limitations

Although the SCAP has basic functions for analyzing CDM data in a distributed network environment, it has the following limitations. First, at this point, the SCAP does not have an access control mechanism for users. If each hospital operates the SS in intervention or hybrid mode, access to the data of the institution can be manually or automatically restricted to users. However, this is undesirable as an access control, and the user's access service should be controlled at the central server group level. Fortunately, the SCAP already uses the AS to authenticate users, and thus it is relatively easy to embed access control into the platform. Second, the SCAP allows CDM data from each hospital to be analyzed individually. However, because medical data show different statistical characteristics according to the geographical location, it is insufficient to analyze hospital data individually through big data analysis. Additional techniques, such as federated learning, should be considered to solve this problem. ### Conclusions

This paper proposed the SCAP, a platform that facilitates an analysis in a distributed environment in which CDM data are stored in the internal network of a hospital. The SCAP consists of the CA, AS, CS, TGS, RS, and SS for safely protecting the data analysis process. We designed and implemented a communication protocol among the components. In addition, some limitations of the platform were discussed. At this point, the SCAP is at the prototype level. In the future, we plan to expand this platform with continuous research and development. **Author Contributions:** Conceptualization, S.J. ; methodology, S.J. ; formal analysis, S.J. ; writing--original draft, S.J. ; visualization, S.J. ; software, C.S. and E.K. ; investigation, C.S. and E.K. ; validation, J.M. ; writing--review and editing, J.M. ; supervision, J.M. ; project administration, J.M. All authors have read and agreed to the published version of the manuscript. **Funding:** This research was supported by a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI) and funded by the Ministry of Health & Welfare, Republic of Korea (Grant Number: HI19C0791). **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## Abbreviations

The following abbreviations are used in this manuscript: \begin{tabular}{l l} EMR & electronic medical record \\ EHR & electronic health record \\ LSH & locality-sensitive hashing \\ CDM & common data model \\ ETL & extract-transform-load \\ AC & analysis codes \\ WI & web interface \\ CA & certificate authority \\ AS & authentication server \\ CS & code signer \\ TCS & ticket-granting server \\ RS & relaying server \\ SS & service server \\ OMOP-CDM & observational medical outcomes partnership CDM \\ OHDISI & observational health data sciences and informatics \\ AR & analysis result \\ REST & representational state transfer \\ PKI & public-key infrastructure \\ SHA & secure hash algorithm \\ JWT & Jwon web token \\ OAuth 2.0 & Open authorization 2.0 \\ \end{tabular} 

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