Differential privacy is a mathematical technique that adds carefully measured randomness to data or to the results worked out from it. The goal is that nobody looking at the output can tell whether any one person’s information was included. For example, a food bank could publish how many families it served in each neighborhood, with each count nudged slightly up or down. The totals stay useful, but no single family can be picked out.
Some organizations use it when they publish statistics or train AI models. The trade-off is accuracy. More privacy means more randomness, and that makes the numbers less precise.
Why it matters
If your organization holds sensitive information about clients, donors or volunteers, differential privacy is one of the few approaches that gives a privacy guarantee you can measure. That sets it apart from de-identification. When a vendor says its AI tool protects the people in your data, ask whether it uses differential privacy, how strong the guarantee is and how much accuracy you give up in return. Those answers also help you weigh risks such as membership inference and compare options like synthetic data.
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