khoaguin/ppml-materials
A compiled list of resources and materials for PPML
When you need to train or use machine learning models on sensitive data without compromising privacy, this resource provides a curated list of research papers and frameworks. It organizes approaches by cryptographic techniques like Homomorphic Encryption and Secure Multi-Party Computation, as well as non-cryptographic methods such as Federated Learning. This is for researchers and practitioners in data science, AI, and cybersecurity who are looking to build or implement privacy-preserving machine learning solutions.
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Use this if you are exploring how to apply machine learning to confidential datasets while ensuring data privacy and regulatory compliance.
Not ideal if you are looking for an off-the-shelf software tool for immediate deployment rather than a collection of research and academic materials.
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May 10, 2025
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