How to: Differential Privacy
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[Differential Privacy](https://www.microsoft.com/en-us/ai/ai-lab-differential-privacy) is a system that simultaneously enables researchers and analysts to extract useful insights from datasets containing personal information and offers stronger privacy protections. This is achieved by introducing "statistical noise".

[Statistical Noise](https://news.microsoft.com/on-the-issues/2020/08/27/statistical-noise-data-differential-privacy/) is a process that small aletrations to masked datasets. The statistical noise hides identifiable characteristics of individuals, ensuring that the privacy of personal information is protected, but it's small enough to not materially impact the accuracy of the answers extracted by analysts and researchers.

[Laplacian Noise](https://en.wikipedia.org/wiki/Laplace_distribution) is a mechanism that adds Laplacian-distributed noise to a function.
Above is a simple diagram of how Differential Privacy-Preserving Data Sharing and Data Mining protects a User's Data