Developing novel secure machine learning methods based on homomorphic encryption

Developing novel secure machine learning methods based on homomorphic encryption schemes is an area of active research. Homomorphic encryption schemes allow data to be processed while still in an encrypted form, making it difficult for malicious actors to gain access to the data and resulting machine learning models. By combining homomorphic encryption and machine learning techniques, researchers are exploring innovative approaches that can effectively protect sensitive data while still allowing for accurate and effective machine learning models.