## 5 Conclusions  
This study investigates the potential of DPFL for public administration services, focusing on both theoretical and practical aspects. DPFL provides a potential approach to data-driven governance, allowing PAs to make use of collaborative data analysis while protecting citizen privacy and encouraging informed decision-making processes. Its main contributions are in establishing the effectiveness of DPFL in reducing the vulnerability of participant data to membership inference attacks via noise injection. It helps to reinforce the practical implications of DPFL in public administration. Beyond privacy-enhancing characteristics, our work provides practical benefits to public administration, including potentially improved service delivery through better and deeper data-driven insights. It provides a better understanding of the technology behind the decision-making support for  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Client 1**} & \multicolumn{2}{c}{**Client 2**} & \multicolumn{2}{c}{**Client 3**} & \multicolumn{2}{c}{**Client 4**} & \multicolumn{2}{c}{**Client 5**} & \multicolumn{2}{c}{**Client 6**} & \multicolumn{2}{c}{**Client 7**} \\ \cline{2-13}
**Noise Multiplier** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** & **Acc** & **F1** \\  & & **Score** & & **Score** & & **Score** & & **Score** & & **Score** & & **Score** & & **Score** \\ \hline
0 (Un-noisy Model) & 0.91 & 0.97 & 0.94 & 0.95 & 0.95 & 0.96 & 0.97 & 0.98 & 0.95 & 0.96 & 0.94 & 0.95 & 0.94 & 0.95 \\ \hline
2 & 0.91 & 0.93 & 0.91 & 0.94 & 0.92 & 0.94 & 0.90 & 0.92 & 0.87 & 0.90 & 0.92 & 0.94 & 0.89 & 0.91 \\
4 & 0.81 & 0.86 & 0.80 & 0.84 & 0.85 & 0.89 & 0.84 & 0.89 & 0.79 & 0.83 & 0.77 & 0.81 & 0.83 & 0.88 \\
6 & 0.63 & 0.64 & 0.64 & 0.66 & 0.68 & 0.73 & 0.75 & 0.82 & 0.67 & 0.71 & 0.68 & 0.72 & 0.64 & 0.66 \\
8 & 0.60 & 0.67 & 0.51 & 0.49 & 0.69 & 0.79 & 0.50 & 0.45 & 0.54 & 0.55 & 0.48 & 0.39 & 0.49 & 0.44 \\
10 & 0.39 & 0.30 & 0.44 & 0.44 & 0.45 & 0.46 & 0.45 & 0.47 & 0.34 & 0.08 & 0.37 & 0.21 & 0.66 & 0.79 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Payroll data: un-noisy and noisy MIA models’ performance (success rates).  
stakeholders, where privacy protection is based on collaborative analysis and modeling, and the efficiency of the public administration process is increased through the use of distributed data resources while maintaining participant anonymity. In this way, citizen privacy is at the top priority.  
Conceptualization, G.N. and M.A.; data curation, M.A.; formal analysis, M.A.; funding acquisition, G.N.; investigation, G.N. and M.A.; methodology, G.N.; project administration, M.A.; resources, G.N.; software, M.A.; supervision, G.N.; validation, M.A.; visualization, M.A.; writing--original draft preparation, M.A. and G.N.; writing--review and editing, M.A. and G.N. All authors have read and agreed to the published version of the manuscript.  
This publication has been written thanks to the support of the Operational Programme Integrated Infrastructure for the project: International Center of Excellence for Research on Intelligent and Secure Information and Communication Technologies and Systems Phase II (ITMS code: 313021W404), co-funded by the European Regional Development Fund (ERDF).  
Data Availability StatementData and code are available at [[https://github.com/privacy-assurance/pa-dpfl](https://github.com/privacy-assurance/pa-dpfl)]([https://github.com/privacy-assurance/pa-dpfl](https://github.com/privacy-assurance/pa-dpfl)) (accessed on 30 April 2024). Anonymized and preprocessed data are available upon request for the private data set under a non-disclosure agreement (NDA).  
The authors declare no conflicts of interest.