About this document
Differential Privacy in Cybersecurity ML by nabeelafatima256 is a document available to read on EtoBox.
This case study presents a differential privacy framework that enables organizations to collaboratively train machine learning models on distributed cyber-security logs while maintaining data privacy. The framework combines federated learning with differential privacy techniques to protect sensitive information during the training process, allowing for improved threat detection without compromising individual data security. Future enhancements aim to optimize privacy controls and computational efficiency, e
- Author
- nabeelafatima256
- Language
- EN