In this paper, we introduce a new multimodal and multilingual public resource for evaluating anonymization models and solutions, comprising a collection of 516 annotated videos. Unlike previously available resources, the Privacy VITA corpus is both multilingual and multimodal, covering Video, Image, Text and Audio modalities. The development of this dataset is motivated by the growing need to anonymize private data in an increasingly multimedia-driven society. Furthermore, emerging regulations pose significant challenges for organizations and companies that manage sensitive information while ensuring legal compliance. This paper details the processes of collection, preprocessing, annotation, and curation of the corpus, as well as its overall scope. We hope this resource will contribute to the advancement of multimodal anonymization research.

Multimodality in Social Sciences and Humanities (SSH) research is often associated with the integration of text and visual data. However, interpreter-mediated telephone interaction presents a different configuration of complexity, where acoustic, temporal, discursive, and pragmatic dimensions converge. This paper presents the design and methodological architecture of PRAGMACOR(Corpus Pragmatics and Telephone Interpreting: Analysis of Face-Threatening Acts, Ref. PID2021-127196NA-I00), a multilingual corpus of interpreter-mediated public service telephone interactions (Chinese–Spanish, English–Spanish, French–Spanish, German–Spanish), as a case study in multimodal and plurilingual SSH infrastructure. The corpus integrates aligned audio recordings, orthographic transcriptions enriched with speech phenomena, temporal segmentation into speech acts, and multilayer pragmatic annotation of Face-Threatening Acts (FTAs), validated through a structured double-annotation and expert review process. Beyond textual data, the infrastructure captures prosodic overlap, turn-taking dynamics, and pragmatic mediation, enabling the study of cross-linguistic transfer and relational negotiation in asymmetrical institutional contexts. Datasets such as PRAGMACOR have proved essential to train LLMs for speech to speech translation (Sakai et al., 2024). Attention is given to the ethical and technical design of the corpus, including local automatic transcription, systematic removal of personal identifiable information, and irreversible voice anonymization through spectral and temporal signal transformation. These procedures ensure both research usability and compliance with responsible data governance principles. By conceptualising interpreter-mediated interaction as an acoustic-discursive multimodal object and plurilingual pragmatic process, this paper argues that PRAGMACOR provides a replicable model for the development of SSH-oriented infrastructures capable of supporting advanced research in multilingual communication, discourse analysis, and future evaluation of language technologies.