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Odyssey: A Systems Approach to Machine Learning Security
This paper provides a systems approach to addressing attacks, consequences, and mitigations for systems using Machine Learning (ML). It explains each of these over the lifecycle of an ML technology, providing clear explanations of what to worry about, when to worry about it, and how to mitigate it while presuming little incoming knowledge of ML specifics. Our discussion of ML vulnerabilities, attacks, and mitigations utilizes the taxonomy developed in NISTIR 8269.
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