Abstract
We introduce a third-party confidentiality-preserving machine learning scheme for soft-failure detection leveraging the robustness of the principal components algorithm to the changes in the rotation of the data axis. We demonstrate that random scrambling of the data is effective to hide sensitive telemetry information.
© 2022 The Author(s)
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