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cs.CR2018
SentiNet: Detecting Localized Universal Attacks Against Deep Learning Systems
Edward Chou, Florian Tramèr, Giancarlo Pellegrino
SentiNet is a novel detection framework for localized universal attacks on neural networks. These attacks restrict adversarial noise to contiguous portions of an image and are reus…
cs.CR2018
Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference
Edward Chou, Josh Beal, Daniel Levy +3
Homomorphic encryption enables arbitrary computation over data while it remains encrypted. This privacy-preserving feature is attractive for machine learning, but requires signific…
cs.CR2018
A Fully Private Pipeline for Deep Learning on Electronic Health Records
Edward Chou, Thao Nguyen, Josh Beal +2
We introduce an end-to-end private deep learning framework, applied to the task of predicting 30-day readmission from electronic health records. By using differential privacy durin…