Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption

• URL: http://arxiv.org/abs/2604.23245v1
• Date: Sat, 25 Apr 2026 10:50:41 GMT
• Title: Training Machine Learning Models on Encrypted Data: A Privacy-Preserving Framework using Homomorphic Encryption
• Abstract summary: Homomorphic encryption enables computations on encrypted data without decryption, thus preserving confidentiality throughout the Machine Learning pipeline.<n>This paper addresses the challenge of training ML models on encrypted data while maintaining accuracy and efficiency.<n>It demonstrates the feasibility of training K-Nearest Neighbors (KNN) and linear regression models on encrypted data, and evaluates encrypted inference for a basic Multilayer Perceptron (MLP) architecture.
• Score: 40.16348991707144
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The use of Machine Learning (ML) for data-driven decision-making often relies on access to sensitive datasets, which introduces privacy challenges. Traditional encryption methods protect data at rest or in transit but fail to secure it during processing, exposing it to unauthorized access. Homomorphic encryption emerges as a transformative solution, enabling computations on encrypted data without decryption, thus preserving confidentiality throughout the ML pipeline. This paper addresses the challenge of training ML models on encrypted data while maintaining accuracy and efficiency by proposing a proof-of-concept for a privacy-preserving framework that leverages Cheon-Kim-Kim-Song (CKKS) for approximate real-number arithmetic. Also, it demonstrates the feasibility of training K-Nearest Neighbors (KNN) and linear regression models on encrypted data, and evaluates encrypted inference for a basic Multilayer Perceptron (MLP) architecture. Experimental results show that models trained under Homomorphic encryption achieve performance metrics comparable to plaintext-trained models, validating the approach. However, challenges such as computational overhead, noise management, and limited support for non-polynomial operations persist. This work lays the groundwork for broader adoption of privacy-preserving ML in real-world applications, balancing security with computational feasibility.

Related papers

• Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models [1.392661575766483]
  Homomorphic Encryption (HE) offers a way to perform computations on encrypted data without decryption during operations.<n>HE is limited to addition and multiplication, making non-linear functions incompatible in their original form.<n>This paper proposes a kernel-based approximation of ReLU, enabling its use within HE-constrained settings.
  arXiv  Detail & Related papers  (2026-05-22T13:54:37Z)
• Continual Pretraining on Encrypted Synthetic Data for Privacy-Preserving LLMs [16.21971760443489]
  We propose an entity-based framework that synthesizes encrypted training data to protect personally identifiable information (PII)<n>Our approach constructs a weighted entity graph to guide data synthesis and applies deterministic encryption to PII entities.<n>Our results on limited-scale datasets demonstrate that our pretrained models outperform base models and ensure PII security.
  arXiv  Detail & Related papers  (2026-01-09T08:44:07Z)
• PRISM: Privacy-preserving Inference System with Homomorphic Encryption and Modular Activation [0.8197459420866039]
  Homomor- phic encryption (HE) offers a solution by enabling computations on encrypted data.<n>HE remains incompatible with machine learning models like convolutional neural networks (CNNs) due to their reliance on non-linear activation functions.<n>This work proposes an optimized framework that replaces standard non-linear functions with homomorphically compatible approximations.
  arXiv  Detail & Related papers  (2025-11-11T03:57:12Z)
• Cryptanalysis via Machine Learning Based Information Theoretic Metrics [58.96805474751668]
  We propose two novel applications of machine learning (ML) algorithms to perform cryptanalysis on any cryptosystem.<n>These algorithms can be readily applied in an audit setting to evaluate the robustness of a cryptosystem.<n>We show that our classification model correctly identifies the encryption schemes that are not IND-CPA secure, such as DES, RSA, and AES ECB, with high accuracy.
  arXiv  Detail & Related papers  (2025-01-25T04:53:36Z)
• Secure numerical simulations using fully homomorphic encryption [0.8739101659113153]
  This work explores the viability of FHE-based, privacy-preserving numerical simulations of partial differential equations.<n>Two Julia packages are introduced, OpenFHE$.$jl and SecureArithmetic$.$jl, which wrap the OpenFHE C++ library.<n>Results show that cryptographically secure numerical simulations are possible, but that careful consideration must be given to the computational overhead and the numerical errors introduced by using FHE.
  arXiv  Detail & Related papers  (2024-10-29T07:47:10Z)
• PEOPL: Characterizing Privately Encoded Open Datasets with Public Labels [59.66777287810985]
  We introduce information-theoretic scores for privacy and utility, which quantify the average performance of an unfaithful user. We then theoretically characterize primitives in building families of encoding schemes that motivate the use of random deep neural networks.
  arXiv  Detail & Related papers  (2023-03-31T18:03:53Z)
• THE-X: Privacy-Preserving Transformer Inference with Homomorphic Encryption [112.02441503951297]
  Privacy-preserving inference of transformer models is on the demand of cloud service users. We introduce $textitTHE-X$, an approximation approach for transformers, which enables privacy-preserving inference of pre-trained models.
  arXiv  Detail & Related papers  (2022-06-01T03:49:18Z)
• Reinforcement Learning on Encrypted Data [58.39270571778521]
  We present a preliminary, experimental study of how a DQN agent trained on encrypted states performs in environments with discrete and continuous state spaces. Our results highlight that the agent is still capable of learning in small state spaces even in presence of non-deterministic encryption, but performance collapses in more complex environments.
  arXiv  Detail & Related papers  (2021-09-16T21:59:37Z)
• Cryptotree: fast and accurate predictions on encrypted structured data [0.0]
  Homomorphic Encryption (HE) is acknowledged for its ability to allow computation on encrypted data, where both the input and output are encrypted. We propose Cryptotree, a framework that enables the use of Random Forests (RF), a very powerful learning procedure compared to linear regression.
  arXiv  Detail & Related papers  (2020-06-15T11:48:01Z)
• CryptoSPN: Privacy-preserving Sum-Product Network Inference [84.88362774693914]
  We present a framework for privacy-preserving inference of sum-product networks (SPNs) CryptoSPN achieves highly efficient and accurate inference in the order of seconds for medium-sized SPNs.
  arXiv  Detail & Related papers  (2020-02-03T14:49:18Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.