Privacy First

In the era of AI, data leakage is a massive risk. Simply hashing names is not enough—re-identification attacks can link anonymized rows back to real individuals using external datasets.

Our Solution: mathematical Noise

Misata uses Differential Privacy (DP) mechanisms (Laplace and Gaussian) to inject statistical noise into the generation process. This ensures that the output data retains the aggregate statistical properties of the original set (like averages and correlations) while making it mathematically impossible to determine if any specific individual's data was included.

"Privacy is not a feature, it's a foundation."