ANN Activation Function Estimators for Homomorphic Encrypted Inference
Homomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computation...
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/11034980/ |
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| author | Mhd Raja Abou Harb Baris Celiktas |
| author_facet | Mhd Raja Abou Harb Baris Celiktas |
| author_sort | Mhd Raja Abou Harb |
| collection | DOAJ |
| description | Homomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments. |
| format | Article |
| id | doaj-art-5f0b2b246de14156bbc45738b19b81eb |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5f0b2b246de14156bbc45738b19b81eb2025-08-20T02:36:59ZengIEEEIEEE Access2169-35362025-01-011310351210353010.1109/ACCESS.2025.357966711034980ANN Activation Function Estimators for Homomorphic Encrypted InferenceMhd Raja Abou Harb0https://orcid.org/0000-0002-8637-5473Baris Celiktas1https://orcid.org/0000-0003-2865-6370Computer Engineering Department, Isik University, Istanbul, TürkiyeComputer Engineering Department, Isik University, Istanbul, TürkiyeHomomorphic Encryption (HE) enables secure computations on encrypted data, facilitating machine learning inference in sensitive environments such as healthcare and finance. However, efficiently handling non-linear activation functions, specifically Sigmoid and Tanh, remains a significant computational challenge for encrypted inference using Artificial Neural Networks (ANNs). This study introduces a lightweight, ANN-based estimator designed to accurately approximate activation functions under homomorphic encryption. Unlike traditional polynomial and piecewise linear approximations, the proposed ANN estimators achieve superior accuracy with lower computational overhead associated with bootstrapping or high-degree polynomial techniques. These estimators are trained on plaintext data and seamlessly integrated into encrypted inference pipelines, significantly outperforming conventional methods. Experimental evaluations demonstrate notable improvements, with ANN estimators enhancing accuracy by approximately 2% for Sigmoid and up to 73% for Tanh functions, improving F1-scores by approximately 2% for Sigmoid and up to 88% for Tanh, and markedly reducing Mean Square Error (MSE) by up to 96% compared to polynomial approximations. The ANN estimator achieves an accuracy of 97.70% and an AUC of 0.9997 when integrated into a CNN architecture on the MNIST dataset, and an accuracy of 85.25% with an AUC of 0.9459 on the UCI Heart Disease dataset during ciphertext inference. These results underscore the estimator’s practical effectiveness and computational feasibility, making it suitable for secure and efficient ANN inference in encrypted environments.https://ieeexplore.ieee.org/document/11034980/Activation function estimatorartificial neural networkencrypted inferencehomomorphic encryption |
| spellingShingle | Mhd Raja Abou Harb Baris Celiktas ANN Activation Function Estimators for Homomorphic Encrypted Inference IEEE Access Activation function estimator artificial neural network encrypted inference homomorphic encryption |
| title | ANN Activation Function Estimators for Homomorphic Encrypted Inference |
| title_full | ANN Activation Function Estimators for Homomorphic Encrypted Inference |
| title_fullStr | ANN Activation Function Estimators for Homomorphic Encrypted Inference |
| title_full_unstemmed | ANN Activation Function Estimators for Homomorphic Encrypted Inference |
| title_short | ANN Activation Function Estimators for Homomorphic Encrypted Inference |
| title_sort | ann activation function estimators for homomorphic encrypted inference |
| topic | Activation function estimator artificial neural network encrypted inference homomorphic encryption |
| url | https://ieeexplore.ieee.org/document/11034980/ |
| work_keys_str_mv | AT mhdrajaabouharb annactivationfunctionestimatorsforhomomorphicencryptedinference AT barisceliktas annactivationfunctionestimatorsforhomomorphicencryptedinference |