# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 2 Materials and Methods
### Secure SASV Module
#### 2.2.5 SASV Validation  
The technological components of the secure voice-based biometric module are evaluated using the ASVSpoof 2024 Challenge corpus (ASVspoof5) [71]. This dataset comprises speech data from over 4000 speakers, containing more than 100,000 genuine utterances and over 500,000 spoofed samples generated using 16 different TTS and voice cloning algorithms. In particular, we consider the dev partition, which includes 140,950 audio samples, covering 8 types of spoofing attacks and more than 700 speakers. Each component of the SASV module is assessed individually using the Equal Error Rate (EER) metric, which corresponds to the operating point where the False Acceptance Rate (FAR) equals the False Rejection Rate (FRR). The evaluation is also conducted in terms of the architecture-agnostic detection cost function (a-DCF), a recently introduced metric for the assessment of SASV systems [72]. The a-DCF reflects the cost of decisions in a Bayes risk sense, with explicitly defined class priors and a detection cost model. Evaluation results are presented in Table 2, reporting EER values for both the ASV and anti-spoofing modules, as well as the combined SASV score. The table also includes the corresponding acceptance probability thresholds used to classify the holder as legitimate at the EER point, in addition to the a-DCF cost. Our results are also compared with those obtained in previous studies. The obtained results are competitive with those reported in the current state of the art. The considered approach achieved the third-best results in terms of a-DCF, after [78]. In addition, note especially that our system is the best model to detect spoofed samples (EER = 0.29%), compared with the rest of the studies. The EER for the overall SASV module in our system is 1.17%, achieved at a decision threshold of 0.21, which defines the acceptance criterion for identifying a legitimate holder. However, this threshold may not represent the optimal operating point for SIBERIA, particularly in applications where minimizing the FAR is crucial for enhancing system security. To maintain both robust authentication and usability, it is essential to select a threshold that strikes an appropriate balance between FAR and FRR. Consequently, the decision threshold should be carefully tuned based on the deployment context and security requirements. To illustrate how the decision threshold can be selected, Figure 4 presents the log-likelihood ratio scores produced by the SASV module over the evaluation dataset. These scores were computed using Equation 2, omitting the sigmoid function to preserve the raw scale of the log-likelihood ratios. This representation facilitates the analysis of score distributions for both genuine, fraudulent, and spoofed trials, enabling the selection of an optimal threshold that best separates the three classes. When assessing a biometric solution such as the one proposed in SIBERIA, it is crucial to evaluate not only its accuracy but also the run-time performance of its different processing stages. Table 3 presents the execution time for each component involved in both enrollment and authentication workflows. The Keys Manager operates on a CPU server equipped with a 24-core Intel (R) Xeon (R) E5-2620 processor (Intel Corporation, Santa Clara, CA, USA) and 64 GB of RAM. The Encrypted Biometry module runs on a separate machine featuring a 16-core Intel (R) Xeon (R) Gold 6130 CPU, 346 GB of RAM, and an NVIDIA (NVIDIA Corporation, Santa Clara, CA, USA) GeForce GTX 1080 Ti GPU with 12 GB of VRAM. Overall, the processing times are consistent and remain low across all stages. The most time-consuming step is the SASV score fusion, performed on encrypted