Securing Transfer-Learned Networks with Reverse Homomorphic Encryption
PositiveArtificial Intelligence
A recent study highlights the importance of securing neural network classifiers that are trained on sensitive data, addressing concerns about training-data reconstruction attacks. The research suggests that differentially private training methods, like DP-SGD, can effectively protect against these vulnerabilities, even when using large datasets. This is crucial as it ensures that the utility of the networks remains intact while safeguarding personal information, paving the way for safer AI applications.
— Curated by the World Pulse Now AI Editorial System

