## 5 Conclusions  
The fuzzy vault scheme is a breakthrough in secure key sharing, seamlessly integrating traditional cryptography with biometric techniques and template protection. This strategy underpins the secure and anonymous usage of drone operators' information. Nevertheless, due to the inherent noise and variability in biometrics, conventional fuzzy vault schemes have been plagued with challenges, including declining recognition rates as chaff points increase, unrealistic runtimes attributed to high-order polynomial reconstruction, and susceptibility to correlation attacks. This paper introduces an enhanced version of the fuzzy vault scheme to tackle these issues. We employ a dual-key approach based on biometric features: the first key is derived from unique operator attributes, such as facial and iris patterns, utilizing confidence interval; the second key, essential for creating a polynomial, is based on what the operator can recall. This dual-key fuzzy vault system facilitates the consistent generation of genuine points in the encoding phase, simplifies extraction during decoding, and assures effective authentication of operators while preserving their anonymity. Importantly, our proposed system has the potential to bring a secure, real-time authentication process to the forefront of drone operations, ensuring the operators' anonymity. Our experimental results, drawn from testing with the AR face database, exhibit improved security and increased accuracy in secret acquisition. Notably, we observed that the encoding and decoding time of the fuzzy vault was at most 3.5 ms across vault sizes ranging from 100 to 1000. Furthermore, the decoding time remained stable despite increasing chaff points, underscoring the system's capacity for real-time polynomial reconstruction and resilience against correlation attacks. These advancements persist regardless of an increase in false vaults, highlighting the robustness of our enhanced fuzzy vault scheme. More importantly, the efficiency and stability of the improved fuzzy vault scheme, coupled with its capacity to withstand real-time demands, make it highly suitable for authenticating drone operators in real-world drone deployments. It provides a secure method for operators to authenticate themselves while maintaining anonymity, thus presenting a tangible solution to drone operators' security and privacy challenges. This substantial development in bio-cryptography not only signifies a future where security, efficiency, and user convenience can harmoniously \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Genuine** & **Chaff** & **Degree of** & **Lock** & **Unlock** \\  & **Points (k)** & **Points (m)** & **Polynomial (d)** & **Time (ms)** & **Time (ms)** \\ \hline \multirow{3}{*}{[38]} & 40 & 100 & 20 & 706.3 & \textless{}25,000 \\  & 40 & 300 & 12 & 593.8 & \textless{}5000 \\  & 40 & 500 & 20 & 1209.6 & \textless{}50,000 \\ \hline \multirow{3}{*}{Ours} & 20 & 100 & 8 & 0.4 & 0.9 \\  & 20 & 500 & 8 & 1.4 & 1.2 \\ \cline{1-1}  & 20 & 1000 & 8 & 1.5 & 1.5 \\ \cline{1-1}  & 20 & 1000 & 10 & 3.6 & 1.1 \\ \cline{1-1}  & 20 & 1000 & 12 & 3.8 & 1.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of the performance with the latest research by [PERSON] et al. [38]. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Unlock Strategy** & **Evaluation** & **Guessing** \\  & **Complexity** & **Complexity** \\ \hline
[38] & Iterated Lagrange with [PERSON] & C(\(k+m\), \(d+1\)) & C(\(k+m\), \(d+1\)) \\ Ours & Iterated Lagrange with Confidence Interval & C(\(k\), \(d+1\)) & C(\(k+m\), \(d+1\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of the Unlock strategy and complexities with the latest research by [PERSON], [PERSON] et al. [38]. coexist, but it also demonstrates a practical application of these principles in the burgeoning field of drone technology without compromising the essential aspect of real-time response.