video1.0<iframe src="https://www.loom.com/embed/e2e21b5f24c84b6787f11bc94cba0c35" frameborder="0" width="1280" height="960" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>9601280Loomhttps://www.loom.com9601280https://cdn.loom.com/sessions/thumbnails/e2e21b5f24c84b6787f11bc94cba0c35-00001.gif268.142Hi everyone, in this video message, I will be introducing Delta Z, the first implementation of Zero Knowledge differential privacy. I will explain the flaws of pseudo-anonymity and the need for a better paradigm like differential privacy. I will also discuss how Delta Z adds noise to the data while maintaining accuracy and privacy. Additionally, I will demonstrate a univariate Gaussian noise additive mechanism and show how privacy budgets can affect the accuracy of query results. Finally, I will touch upon the potential applications of Delta Z, from crowdsource dynamic databases to trustless training of machine learning models.