# Blind Faith: Privacy-Preserving Machine Learning using Function Approximation

arXiv: https://arxiv.org/abs/2107.14338 · HTML: https://mlsanity.com/paper/2107.14338/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Jul 2021.

## Built on by (most cited first, 10 of 10)

- [Split Ways: Privacy-Preserving Training of Encrypted Data Using Split Learning](https://mlsanity.com/paper/2301.08778/) (2023)
- [Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2309.10517/) (2023)
- [GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2401.14840/) (2024)
- [Split Without a Leak: Reducing Privacy Leakage in Split Learning](https://mlsanity.com/paper/2308.15783/) (2023)
- [Make Split, not Hijack: Preventing Feature-Space Hijacking Attacks in Split Learning](https://mlsanity.com/paper/2404.09265/) (2024)
- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [Towards Privacy-Preserving Neural Architecture Search](https://mlsanity.com/paper/2204.10958/) (2022)
- [Securing Neural Networks with Knapsack Optimization](https://mlsanity.com/paper/2304.10442/) (2023)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)
- [DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference](https://mlsanity.com/paper/2512.17398/) (2025)

## Abstract (arXiv)

Over the past few years, a tremendous growth of machine learning was brought about by a significant increase in adoption of cloud-based services. As a result, various solutions have been proposed in which the machine learning models run on a remote cloud provider. However, when such a model is deployed on an untrusted cloud, it is of vital importance that the users' privacy is preserved. To this end, we propose Blind Faith -- a machine learning model in which the training phase occurs in plaintext data, but the classification of the users' inputs is performed on homomorphically encrypted ciphertexts. To make our construction compatible with homomorphic encryption, we approximate the activation functions using Chebyshev polynomials. This allowed us to build a privacy-preserving machine learning model that can classify encrypted images. Blind Faith preserves users' privacy since it can perform high accuracy predictions by performing computations directly on encrypted data.