# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 6 Conclusions and Future Work  
We propose a novel technique for combining GSs with SA to manage privacy preservation problems in FL. Instead of the conventional approach (i.e., of sharing secret keys between clients), we shared the secret key between the admin and client. The admin is already trusted to generate a GS, and generating secure keys on its side eases the burdens upon other clients. Detailed numerical analysis is provided to verify that the proposed algorithm achieves significantly lower computational and communication costs than existing algorithms for the iterative FL process. The number of transmissions required to complete the communication was also much lower under our algorithm. Simulation results show that our algorithm can effectively preserve the privacy of both the data and users than existing algorithms. We also provided an AVISPA-based security analysis to show that our proposed algorithm is resistant to numerous different security attacks.  
Our future work will focus on reducing the number of computations required to reduce the overall complexity while also exploiting the iterative process in FL. Another important future work for this paper is to reduce the dependency on admin. The basic assumption of our paper is that the admin is a completely trusted entity, and it must be available all the time during the FL process. Clients and servers both depend upon the admin for authentication purposes. In real-life scenarios, it can be the limitation of the GS approach. In our future work, we will also consider cases where an admin is unavailable,  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{3}{c}{**Number of Signals per Iteration**} \\ \cline{2-3}  & \(f(n,m)\) & \(n=1000,m=2000\) \\ \hline [PERSON] et al. [24] & \(m+m+m+n+n+1+m\) & 11,001 \\ [PERSON] et al. [1] & \(m+m+n+n+1+n+m\) & 10,001 \\ [PERSON] et al. [5] & \(m+m+n+n(n-1)+n+1+m\) & 1,006,001 \\ [PERSON] et al. [23] & \(m+n(n-1)/100+n+m\) & 14,990 \\ [PERSON] et al. [22] & \(m+m+n+n(n-1)+n+1+m\) & 1,006,001 \\ Proposed & \(m+n+1+m+m+n+1+1\) & 8003 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Comparison of signaling overhead with existing algorithms.  
Figure 9: Comparison of signaling cost of algorithms.  
or the admin is a compromised entity. We will explore the problems caused by the situation and try to find out optimal solution to it.  
Conceptualization, S.K. and B.J.C.; methodology, validation, and formal analysis, S.K.; investigation, J.W.J. and J.Y.Y.; resources, J.W.J. and J.Y.Y.; writing, S.K. and J.W.J.; review and editing, B.J.C.; supervision, B.J.C.; project administration, B.J.C.; funding acquisition, B.J.C. All authors have read and agreed to the published version of the manuscript.  
This research work was supported by the MSIT Korea under the NRF Korea (NRF 2022R1A2C4001270) and the Information Technology Research Center (ITRC) support program (IITP-2024-2020-0-01602) supervised by the IITP.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding authors.  
The authors declare no conflicts of interest.