Title: Secure Domain Adaptation with Multiple Sources. Abstract: Multi-source unsupervised domain adaptation (MUDA) is a recently explored  learning framework within UDA, where the goal is to address the challenge of annotated data scarcity in a target domain via transferring knowledge from multiple source domains with annotated data.  When the source domains are distributed, data privacy and security can become a significant concern, e.g., medical domains, yet existing MUDA methods overlook this concern. We develop an algorithm to address MUDA when source domains' data cannot be shared.  Our method is based on aligning the distributions of the source and target domains indirectly via  internally learned distributions in an intermediate embedding space. Our  theoretical analysis supports our approach and extensive empirical results demonstrate our algorithm is effective and compares favorably against existing MUDA methods.