# Siberia: A Self-Sovereign Identity and Multi-Factor Authentication Framework for Industrial Access
## 1 Introduction
### Secure Voice Biometric Authentication  
One of the most critical aspects in the design of SSI systems is ensuring that VCs are presented exclusively by their rightful holder. Biometric authentication serves as a strong complement to traditional username-password mechanisms, enhancing protection against unauthorized access. Various human traits, such as the retina, face, and fingerprint, can be used to verify the identity of a VC holder. Among these, voice offers several advantages for biometric applications. It can be captured remotely, requires no physical contact, and does not necessitate specialized hardware, as microphones are already embedded in most consumer devices [29]. Automatic Speaker Verification (ASV) systems have gained popularity in recent years, largely due to advances in deep learning [30]. Modern ASV systems typically rely on large-scale Deep Neural Networks (DNNs) trained to extract speaker embeddings that represent an individual's vocal identity [31]. The most well-known DNN architectures used for this purpose include X-vectors [32], ECAPA-TDNN [33], and, more recently, TITANet [34]. A typical ASV system comprises two main stages: (1) enrollment, during which a set of utterances from the holder is used to extract a voiceprint (referred as a speaker embedding) using one of the previously mentioned DNN models; and (2) authentication, where a new speech sample from the speaker is processed to extract a second voiceprint. This speaker representation is then compared to the one obtained during enrollment in order to retrieve a similarity score used for final authentication when it surpasses a predefined threshold. Common techniques for comparing speaker voiceprints include simple metrics such as cosine similarity, as well as more sophisticated and adaptive methods like Probabilistic Linear Discriminant Analysis (PLDA) [35]. Despite the multiple benefits of ASV systems, their performance is known to degrade significantly in the presence of fabricated or manipulated speech inputs [36]. These so-called spoofing attacks can be categorized as either logical or physical. Logical attacks involve the use of voice cloning or text-to-speech (TTS) systems to synthetically generate a speaker's voice, whereas physical attacks rely on replaying pre-recorded speech samples of the legitimate user during the authentication phase [36]. Among these, logical attacks currently pose the greatest challenge due to recent advancements in generative AI technologies and modern speech synthesis frameworks [37]. With the aim of improving the robustness of ASV systems against spoofing attacks, it is essential to incorporate anti-spoofing mechanisms capable of detecting machine-generated or replayed speech samples [38]. Given the importance of creating these mechanisms, the research community has actively developed and benchmarked a wide range of models and detection engines [39]. The most common approach to integrate an anti-spoofing mechanism in an ASV system is to design separate modules that can be combined in a cascade or parallel fashion. This integration can be performed at the score, decision, or embedding levels. This fusion of an ASV with an anti-spoofing mechanism is what is known in the research community as SASV systems [40]. When implementing these biometric systems, it is crucial to ensure the security of all data, including the voiceprints derived from the VC. According to the European Union's data privacy regulations [41], biometric data are classified as personal and highly sensitive information, thereby warranting strong privacy protections. In line with this, the ISO/IEC IS 24745 standard [42] specifies requirements for the protection of biometric information, addressing confidentiality, integrity, and renewability/revocability during both storage and transmission.