## 4 Experimental Results
### Comparison with Related Works  
In this section, we compare our approach with related works in the aspect of security. From Table 5, we compare with the related works [17; 29; 65; 66]. Now, we will analyze the mentioned methods in detail.  
[PERSON] et al. [29] proposed alignment-free fingerprint cryptosystem based on fuzzy vault. Since the stored data contains the biometric feature, an attacker can retrieve biometric data. [PERSON] et al. [67] improved the fuzzy commitment scheme. If parameters of \(S\) and \(P\) are compromised, the biometric template and bio-key can be retrieved by using stored data in a database. [PERSON] et al. [17] put forward a DNN model to generate the bio-key from the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & \(\mu\) & \(\sigma\) & **Degree of Freedom (N)** & \(BF\)**Trials** \\ \hline ORL & 0.4996 & 0.0232 & 464 & \(2^{417}\) \\ Extended YaleB & 0.4934 & 0.0241 & 431 & \(2^{387}\) \\ CMU-PIE & 0.4980 & 0.0247 & 407 & \(2^{366}\) \\ \hline \hline \end{tabular}
\end{table}
Table 4: Degree of freedom computation for the mapped binary code at the length of 1024 bits.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Biometrics** & **Storage Data** & **Technique Scheme** & **Resist Information Leakage** \\ \hline [PERSON] et al. [29] & Fingerprint & chaff points & fuzzy vault & NO \\ [PERSON] et al. [67] & Iris & Helper data & fuzzy commitment & NO \\ [PERSON] et al. [17] & Retinal & Biometric template & DNN & NO \\ [PERSON] et al. [68] & Fingerprint & Helper data & Key binding & NO \\ Ours & Face & AD and PV & DNN & Yes \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparisons with other related works.  
retinal image. However, if the extract biometric template is compromised, the new template cannot be regenerated. [PERSON] et al. [68] designed a new key binding with biometric data via objective function. Nevertheless, the security actually depends on the predefined threshold value. If this threshold value is obtained, the stored helper data may reveal biometric data. Therefore, these methods are vulnerable to information leakage attack.  
To avoid the information leakage of biometric data and bio-key, we combine DNN architecture and fuzzy commitment to generate the bio-key. During enrollment, we assign random binary code to each user, which is shuffled via PV to generate permuted code. Next, the binary codes are encoded by the fuzzy commitment module to yield AD. Finally, PV and AD are stored in the database. During bio-key reconstruction, we utilize the DNN to generate binary code, and the final bio-key is restored through our scheme. It can be observed that input binary codes of the fuzzy commitment encoder are uniformly distributed and are not related to biometric data during the enrollment stage. Hence, it does not expose biometric data and bio-key when PV and AD are public. Additionally, we can modify the permutation seed to update a new pair of PV and AD for the same user, which can also resist cross-matching attack.  
Overall, our proposed approach is more robust against information leakage attack and cross-matching attack. Moreover, to meet the different sizes of the key in the encryption application, our model can be easily modified by only setting the output dimension in the DNN architecture, which means that our accuracy can be well preserved in different key lengths.