Privacy-Preserving Machine Learning with Homomorphic Encryption
By Zafri
Categories: Cybersecurity
About Course
- Tech Stack: Python (PyCryptodome, TensorFlow, PyTorch), Microsoft SEAL, AWS (SageMaker, KMS), Docker
- Objective: Implement a privacy-preserving machine learning system using homomorphic encryption. The system should allow encrypted data to be used for model training and inference without compromising privacy. Use Microsoft SEAL for encryption libraries and TensorFlow or PyTorch for machine learning models.
- Skills Needed: Homomorphic encryption, privacy-preserving machine learning, cryptography (PyCryptodome), secure model deployment (AWS), Docker for containerization.
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