# A Framework for Attribute-Based Access Control in Processing Big Data with Multiple Sensitivities
## 3 Results
### Analysis and Observations
#### 3.4.4 Performance  
We analyzed the performance of Apache Hadoop under different security configurations. We measured the elapsed execution time for each sequential job. The workflow of data inputs, PySpark programs, and outputs was assigned attributes and tags by using Apache Atlas, as shown in Figure 12.  
The elapsed execution time was recorded from the Hadoop Resource Manager's web GUI under different security configurations, as listed in Table 4 and shown in Figure 13. The configurations were as follows: execution with the Ranger plugins disabled, execution as a local privileged user of Hadoop, execution with resource-based AC by using policies of Apache Ranger, and execution with tag-based AC that uses both the polices of Apache Ranger and the metadata of Apache Atlas. The fourth configuration corresponds to an ABAC approach. The performance analysis did not yield any significant differences in terms of executing the sequence of data processing programs (PySpark programs) in the use case for the lifecycle of the data. Overall, we noted similar program execution times inall three configurations. This indicates that AC security services had a very limited impact on the performance of the system. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **PySpark** \\ **Program** \\ \end{tabular} } & \multirow{2}{*}{**Description**} & \multicolumn{4}{c}{**Elapsed Times (s) (Average of 15 Executions)**} \\ \cline{3-6}  & & **Range Plugins** & **Local** & **Resource-Based** & **Tag-Based AC** \\ \hline
1–Filter-job & Filters JSON data from the Twitter dataset & & & & & \\
59 FsocialPy & and puts it into Spark DataFrames & & & & & \\ \hline
2–Filter-job & Filters HLT JSON data from a healthcare & & & & & \\ Synthea.py & dataset and puts it into Spark DataFrames & & & & & \\ \hline
3–Join-job.py & Joins the social media and healthcare & & & & & \\ \multirow{2}{*}{3–Join-job.py} & datasets based on the social media & & & & & \\  & messages and healthcare condition for & & & & & \\  & each patient & & & & & \\ \hline
4–Privacy-job.py & Hashes and encrypts PII and HIPAA data & & & & & \\ \hline
5–Analysis-job & & & & & & \\ \multirow{2}{*}{Hash.py} & analyzes hashed data from the private job & & & & \\  & to produce the number of messages per & & & & \\  & medical condition & & & & \\ \hline
6–Analysis-job & & & & & & \\ \multirow{2}{*}{Encrypt.py} & analyzes the encrypted data from the private job to generate the number of messages per medical condition & & & & \\  & messages per medical condition & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of AC security on program execution time. Figure 12: Configuration of data processing and propagation of attribute classification in Atlas. Figure 13: Elapsed times for executions of the PySpark program under different configurations. Although at least minor differences in performance were expected when executing the lifecycle of the data under different security configurations, we needed to scale out significantly to observe such impacts on performance according to performance analyses reported in the industry ([[https://privacera.com/blog/immuta-vs-apache-ranger-a-failed-benchmark/](https://privacera.com/blog/immuta-vs-apache-ranger-a-failed-benchmark/)]([https://privacera.com/blog/immuta-vs-apache-ranger-a-failed-benchmark/](https://privacera.com/blog/immuta-vs-apache-ranger-a-failed-benchmark/)) accessed 14 January 2023). We estimated that we needed to scale up from 14 GB to over 1 TB of data, which would require six or more Data Nodes for the observation of a minor (under 10%) impact on performance. As the size of the dataset and the complexity of the processing lifecycle were scaled out, the impact of performance could have been identified by the increase in the elapsed time. However, the overhead due to AC services provided by Apache Ranger was expected to be minimal. The encryption of the data and Kerberos led to a more significant increase in the elapsed time.