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SOTERIA: In Search of Efficient Neural Networks for Private Inference
[article]
2020
arXiv
pre-print
ML-as-a-service is gaining popularity where a cloud server hosts a trained model and offers prediction (inference) service to users. In this setting, our objective is to protect the confidentiality of both the users' input queries as well as the model parameters at the server, with modest computation and communication overhead. Prior solutions primarily propose fine-tuning cryptographic methods to make them efficient for known fixed model architectures. The drawback with this line of approach
arXiv:2007.12934v1
fatcat:tdch7v4uu5e3dbokfrtl27mgum