Collaborative medical research increasingly relies on the aggregation and analysis of diverse datasets spanning multiple institutions. However, the sensitive nature of patient health information necessitates robust mechanisms to protect individual privacy. This article delves into the critical landscape of privacy-preserving data sharing techniques in medical research. It examines the ethical and legal imperatives driving the need for such methods, explores a spectrum of established and emerging technologies including anonymization, encryption, and federated learning, and discusses their respective strengths, limitations, and applicability within the complex context of medical data. By analyzing the current state of the art and highlighting future directions, this paper underscores the vital role of privacy-preserving approaches in fostering collaborative investigation while upholding the fundamental right to patient confidentiality.