Approximate Homomorphic Pre-Processing for CNNs

Shabnam Khanna; Ciara Rafferty

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Paper

Approximate Homomorphic Pre-Processing for CNNs

Topics: Applied Cryptography; Privacy Enhancing Technologies; Security and Privacy in the Cloud

In Proceedings of the 20th International Conference on Security and Cryptography SECRYPT - Volume 1, 710-715, 2023 , Rome, Italy

Authors: Shabnam Khanna and Ciara Rafferty

Affiliation: CSIT, Queen’s University Belfast, U.K.

Keyword(s): Homomorphic Evaluation, Approximate Computing, ReLU, Convolutional Neural Networks.

Abstract: Homomorphic encryption (HE) allows computations on encrypted data, making it desirable for use in privacy-preserving data analytics. However, HE function evaluation is computationally intensive. Approximate computing (AC) allows a trade-off between accuracy, memory/energy usage and running time. Polynomial approximation of the Rectified Linear Unit (ReLU) function, a key CNN activation function, is explored and AC techniques of task-skipping and depth reduction are applied. The most accurate ReLU approximations are implemented in nGraph-HE’s Cryptonets CNN using a SEAL backend, resulting in a minimal decrease in training accuracy of 0.0011, no change in plaintext classification accuracy, and a speed-up of 47%.

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Paper citation in several formats:

Khanna, S. and Rafferty, C. (2023). Approximate Homomorphic Pre-Processing for CNNs. In Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT; ISBN 978-989-758-666-8; ISSN 2184-7711, SciTePress, pages 710-715. DOI: 10.5220/0012085400003555

@conference{secrypt23,
author={Shabnam Khanna. and Ciara Rafferty.},
title={Approximate Homomorphic Pre-Processing for CNNs},
booktitle={Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT},
year={2023},
pages={710-715},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012085400003555},
isbn={978-989-758-666-8},
issn={2184-7711},
}

TY - CONF

JO - Proceedings of the 20th International Conference on Security and Cryptography - SECRYPT
TI - Approximate Homomorphic Pre-Processing for CNNs
SN - 978-989-758-666-8
IS - 2184-7711
AU - Khanna, S.
AU - Rafferty, C.
PY - 2023
SP - 710
EP - 715
DO - 10.5220/0012085400003555
PB - SciTePress