- 👋 Hi, I’m @SagerKudrick
- 📫 reach me at sagerkudrick@hotmail.com
Feel free to explore my repository at https://github.com/SagerKudrick/ml-dp, where you can witness an innovative application of Differential Privacy in conjunction with state-of-the-art Machine Learning models. This demonstration focuses on the renowned MNIST dataset, which involves the classification of hand-written digits ranging from 0 to 9. By incorporating differential privacy techniques, we not only achieve exceptional accuracy in our model's predictions but also uphold the utmost privacy and security of the underlying training datasets.
Within the repository, you will discover a comprehensive showcase of how Differential Privacy can seamlessly integrate with modern Machine Learning techniques. Through meticulous implementation and rigorous experimentation, we have demonstrated that the incorporation of differential privacy does not compromise the accuracy or performance of our models. On the contrary, it serves as a robust safeguard that protects sensitive data from potential breaches while enabling precise predictions.