Differential Privacy is instrumental in facilitating safe data sharing in research, particularly in fields handling sensitive information. Implementing mechanisms that inject noise into data or analysis processes, allows for the sharing of insights and aggregate information without compromising individual privacy. This is crucial in research environments where data sharing and collaboration are essential but privacy concerns are paramount. Differential Privacy enables researchers to access and analyse data safely, fostering collaboration and innovation while adhering to ethical standards of data privacy. This approach is particularly beneficial in social and health sciences, where sensitive data is often critical for research but requires stringent privacy protection.