This paper addresses the problem of language mismatch in self-supervised learning (SSL)-based speaker anonymization systems. The authors propose techniques to improve cross-lingual robustness of anonymized speaker representations, ensuring privacy protection without sacrificing intelligibility and naturalness across different languages.