The Privacy-Preserving Deep Learning (PPDL) group is dedicated to advancing techniques that ensure the confidentiality and security of sensitive data in deep learning applications. Our research focuses on developing and implementing methods such as federated learning, differential privacy, anonymization, and multi-party computation. We work with a diverse range of data types, including images, speech, and text, with a particular emphasis on medical data. Our goal is to create robust and secure AI solutions that protect patient privacy while enabling cutting-edge medical research and healthcare innovations. Through our multidisciplinary approach, we aim to set new standards for privacy in AI and contribute to the ethical advancement of technology in medicine.