## 1 Introduction  
Public healthcare has transformed from treatment to proactive prevention and management of disease. Clinical research is based on patient data derived from hospitals participating in a distributed research network [1]. Observational databases vary in both purpose and design. To enhance the collaborative work in the distributed network, interoperability in healthcare is focused on exchanging healthcare data between multiple hospital systems in a distributed research network. The Common Data Model (CDM) represents healthcare data from diverse health records from hospitals. It is used for observational research in a distributed research network [2, 3]. It consists of international standard terms with different elements according to the purpose [2, 3, 4]. CDM is a data standard that is proposed to standardize the structure and content of observational data. It is used to enable distributed collaborative research and support to enable efficient analyses that can produce reliable evidence from a variety of data sources across multiple institutions. The Observational Health Data Sciences and Informatics (OHDSI) community proposes OMOF, one of the representative CDM standards, which allows for the systematic analysis of disparate observational databases. With the increasing volume of electronic data in the field of healthcare, provider organizations need to handle identity governance to proactively mitigate high-risk access before a personal data breach occurs. A data breach can be conducted by attacks, including stealing data through insider leaks or exploiting software or hardware vulnerabilities [5]. Distributed storing encrypted data is required to prevent those problems. To overcome the problem caused by data breach, we generate the encrypted CDM and securely deliver it to IPFS (Inter Planetary File System) managed by the cloud CDM. The cloud CDM is introduced to support CDM derived from HIS (Hospital Information System) and its operation scheme among hospitals in the cloud CDM [6]. Digital identity takes a crucial role as the body of information about an individual used by computer systems to identify and establish trust among organizations [7; 8]. A distributed research network composed of researchers handling medical information has several digital identities associated with their activity. An identification scheme to preserve the role of a researcher is required. Blockchain has tamper-proof Distributed Ledger Technology (DLT), which is used to record the transaction permanently [9; 10; 11]. Blockchain is originated to regard decentralized control and immutability of event logs [9]. It suggests adopting a distributed peer-to-peer timestamp server to solve the double-spending issue. With regard to the transactions recorded in the ledger, blockchain fosters benefits, including auditability, chain traceability, and high transparency. Its characteristics contain consensus mechanisms, privacy set- tings, transparency, immutability, and decentralized control [12]. OmniPHR focuses on personal health records, which allow people to manage their medical records, distribution, and interoperability. It entails dividing PHR into data chunks that are dispersed over a P2P network [4]. The high cost of a central authority of identity management can be reduced by DLT. Therefore, we consider utilizing the distributed ledger and the node-to-node consensus method to build a trusted digital identity management system in an untrusted environment. The issues of identity fragmentation, theft, and single points of failure in conventional centralized identity management systems are resolved by enabling users to manage their identities independently of a third party [13; 14]. Due to the features of the distributed ledger, which enable institutions to communicate with one another while maintaining the requirement that each institution always abides by all applicable legislation, DLT provides an alternate solution.