# Centralized vs. Decentralized Cloud Computing in Healthcare
## 6 Decentralized Cloud Computing
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Ref** & **Year** & **Proposed Model** & \multicolumn{2}{c}{**Strengths**} & \multicolumn{2}{c}{**Limitations**} \\ \hline
[69] & 2013 & The architecture synthesizes decentralized data repositories with an advanced visual information processing algorithm. & \(\bullet\) & Enables the efficient storage and transmission of large medical image datasets. & \(\bullet\) & The document does not cover specific methods for ensuring the privacy of patient data \\ \hline
[71] & 2013 & An integrated data management framework for digital health platforms, leveraging both the RDBMS and NoSQL database paradigms across decentralized infrastructure. & \(\bullet\) & Efficiently handles diverse data types. & \(\bullet\) & Combining RDBMS and NoSQL is complex and requires expertise in both technologies. \\ \hline
[66] & 2017 & A decentralized ecosystem facilitating medical data interoperability, leveraging ubiquitous mobile computing platforms. & \(\bullet\) & Quick access to patient data. Real-time data exchange with minimal latency. & \(\bullet\) & The capabilities of smartphones are limited by storage, processing power, and battery life. \\ \hline
[67] & 2018 & An innovative hybrid cloud infrastructure, termed “MedShare,” orchestrates the collaborative utilization of medical resources across autonomous healthcare entities. & \(\bullet\) & The integration and interoperability of legacy EHR systems. MedShare is free to use and share. & \(\bullet\) & High response time and timeouts in the query-record step between private clouds. \\ \hline
[73] & 2019 & A community cloud architecture that aims to balance data accessibility and security compliance for healthcare big data applications. & \(\bullet\) & Fast processing and scalable data analytics. Low-latency access to data. & \(\bullet\) & Query parallelization increases hardware costs and power usage. \\ \hline
[68] & 2020 & Design of a retrieval and storage-based indexing framework (RSIF) for distributed cloud computing environments. & \(\bullet\) & Low mean retrieval time. & \(\bullet\) & Continuous indexing slows down processes and consumes significant computing resources for large datasets like patient data. \\ \hline
[61] & 2021 & Decentralized personal cloud data model integrated into the Campus Health Information System (CHIS) for storing and managing student health data. & \(\bullet\) & Avioids the performance bottleneck. & \(\bullet\) & Continuous indexing slows down processes and consumes significant computing resources for large datasets like patient data. \\ \hline
[72] & 2021 & This framework seeks to enhance data integrity and confidentiality safeguards for health-related information within a distributed digital repository ecosystem. & \(\bullet\) & Avioids the performance bottleneck. & \(\bullet\) & Student devices may lack security controls and maintenance, making them more vulnerable to security risks and unauthorized access. This could potentially compromise the confidentiality of personal health data stored on them. \\ \hline
[70] & 2023 & The DDAOM is a model for efficiently managing large healthcare data in mobile cloud computing. & \(\bullet\) & Reduces storage delays. & \(\bullet\) & Large AES 256 encryption key sizes slow down data access and delay efficient end-user communication. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of the extant decentralized cloud computing studies.