# Cloud Security Using Fine-Grained Efficient Information Flow Tracking
## 4 CloudMonitor as Our Conceptual Framework  
The CloudMonitor framework is a pragmatic approach that can monitor and control data flow between the consumer and the CSP. Moreover, CloudMonitor enables consumers to review their data housed in the cloud. This is achieved by connecting data propagation policies through the utilization of security labels embedded within the data intended for transfer. Thus, relying on these consumers' labels can control how their data propagates when initially transferred or uploaded to the cloud service separate from the CSP. Additionally, the user's browser and the client API of the CSP service are modified in a way that detects every upload that is being executed to monitor employee actions in relation to the data they have placed in the cloud. The next part of the CloudMonitor is a service provided by CSP to the consumer. For this framework part, the CSP incorporates a simple API, whereby DFT is integrated into their offered services. With the DFT API, a CSP can label any sensitive data that requires additional protection. The primary objective of this second capability is to guarantee that when a user uploads data to the cloud, all operations are meticulously recorded, actions are linked to the authorized user, and access to this data is restricted to specific network groups. This can give more confidence to consumer organizations in adopting a CSP for a portion of their data transfer and storage requirements, at the very least. Likewise, the CloudMonitor framework is prepared to support consumer organizations with many employees who would concurrently propagate significant amounts of data that do not carry the same tags. As a result, the data tracking level is executed at a level of fine-grain to cover any sensitive data requirements through a complete set of information tracking constructs, and the \"Time-Controllable Keyword Search Scheme\" for mobile e-health clouds has more specific focuses. The CloudMonitor conceptual framework, depicted in Figure 6, outlines the process of data contamination and subsequent tracking within cloud environments through three main steps:  
1. **Tainting Data at the Source:** Initially, sensitive information is labeled or \"tainted\" at the points of entry where it is transferred to the cloud. This step involves monitoring the flow of data both into the cloud (to prevent leakage) and within the consumer network (to detect potential contamination, such as malware). Key points of potential data leakage, such as Cloud-client APIs, user browsers, and local consumer premises, are identified as \"taint sinks\" where data inspection occurs to assess sensitivity and take appropriate actions. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Data Block Size in Bytes**} & \multicolumn{2}{c}{**Native**} & \multicolumn{2}{c}{**Pintool**} & \multicolumn{2}{c}{**Libdft**} \\ \cline{2-7}  & **Mean** & **Std Dev** & **Mean** & **Std Dev** & **Mean** & **Std Dev** \\ \hline
120 & 0.20 & 0.010 & 10.20 & 0.20 & 8.10 & 0.12 \\
256 & 0.43 & 0.021 & 15.10 & 0.50 & 10.43 & 0.31 \\
512 & 1.50 & 0.32 & 34.15 & 2.11 & 27.40 & 0.50 \\
1024 & 2.21 & 1.50 & 100.23 & 15.23 & 75.30 & 10.30 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Bandwidth Comparison Using Imbench’s (In Mb/S). 2. **Enforcing Propagation Policy:** The propagation policy governs how tainted data moves from the sources to the sinks. It determines the granularity and precision of taint tracking, ensuring that data is tagged at the most detailed level to achieve appropriate policy-driven tracking accuracy and reliability. 3. **Sanitizing Data at the Sink:** Finally, data is sanitized or reviewed for sensitivity at the sink points before further processing or storage. This step ensures that only authorized data is retained and that appropriate actions are taken to mitigate any risks identified during the tracking process.