Using a shared random unitary transform on image features preserves SVM kernel values for linear, RBF, and polynomial kernels, allowing privacy-preserving cloud classification without accuracy loss under a common secret key.
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Privacy-Preserving Support Vector Machine Computing Using Random Unitary Transformation
Using a shared random unitary transform on image features preserves SVM kernel values for linear, RBF, and polynomial kernels, allowing privacy-preserving cloud classification without accuracy loss under a common secret key.