Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption

We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure... (read more)

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