Differentially private algorithms offer several key properties and advantages. Firstly, they are future-proof, meaning their privacy guarantees remain intact irrespective of any additional external information or future data releases. They provide a quantifiable level of privacy, indicated by the epsilon parameter, and this guarantee extends to groups of individuals. Crucially, differentially private algorithms allow for composition, enabling the control and understanding of cumulative privacy loss across multiple analyses. This property allows for complex data analyses while managing overall privacy risks. Additionally, differential privacy ensures that findings from data analysis are generalisable to the broader population, making it a powerful tool for conducting valid statistical research that respects individual privacy.