Integrity and privacy in adversarial machine learning
Machine learning is being used for an increasing number of applications with societal impact. In such settings, models must be trusted to be fair, useful, and robust. In many applications, a large amount of training data is collected from a variety of sources, including from private or untrusted ind...
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| Online Access: | http://hdl.handle.net/2047/D20413920 |