Differential privacy provides robust privacy protection by incorporating a mathematical framework that remains effective regardless of future data developments or advancements in technology. This 'future-proofing' aspect ensures that the privacy guarantees made today will still be valid in the future, even as new data becomes available or new analytical methods are developed. The approach is designed to withstand attempts to compromise privacy using additional external information or advanced processing techniques. By basing privacy protection on the process of output generation, rather than on the specifics of the current data landscape, differential privacy ensures enduring privacy safeguards.