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In this paper, a secure Convolutional Neural Network classifier is proposedusing Fully Homomorphic Encryption (FHE). The secure classifier provides a userwith the ability to out-source the computations to a powerful cloud serverand/or setup a server to classify inputs without providing the model orrevealing source data. To this end, a real number framework is developed overFHE by using a fixed point format with binary digits. This allows for realnumber computations for basic operators like addition, subtraction, andmultiplication but also to include secure comparisons and max functions.Additionally, a rectified linear unit is designed and realized in theframework. Experimentally, the model was verified using a Convolutional NeuralNetwork trained for handwritten digits. This encrypted implementation showsaccurate results for all classification when compared against an unencryptedimplementation.

Secure Convolutional Neural Network using FHE