# GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption

arXiv: https://arxiv.org/abs/2401.14840 · HTML: https://mlsanity.com/paper/2401.14840/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 32 references that are in the map.
- First posted on arXiv 26 Jan 2024.

## Builds on (most cited first, 8 of 8)

- [Can We Use Split Learning on 1D CNN Models for Privacy Preserving Training?](https://mlsanity.com/paper/2003.12365/) (2020)
- [Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data](https://mlsanity.com/paper/1911.07101/) (2019)
- [POSEIDON: Privacy-Preserving Federated Neural Network Learning](https://mlsanity.com/paper/2009.00349/) (2020)
- [Blind Faith: Privacy-Preserving Machine Learning using Function Approximation](https://mlsanity.com/paper/2107.14338/) (2021)
- [Love or Hate? Share or Split? Privacy-Preserving Training Using Split Learning and Homomorphic Encryption](https://mlsanity.com/paper/2309.10517/) (2023)
- [Learning in the Dark: Privacy-Preserving Machine Learning using Function Approximation](https://mlsanity.com/paper/2309.08190/) (2023)
- [A More Secure Split: Enhancing the Security of Privacy-Preserving Split Learning](https://mlsanity.com/paper/2309.08697/) (2023)
- [PocketNN: Integer-only Training and Inference of Neural Networks via Direct Feedback Alignment and Pocket Activations in Pure C++](https://mlsanity.com/paper/2201.02863/) (2022)

## Built on by (most cited first, 7 of 7)

- [Wildest Dreams: Reproducible Research in Privacy-preserving Neural Network Training](https://mlsanity.com/paper/2403.03592/) (2024)
- [Towards Friendly AI: A Comprehensive Review and New Perspectives on Human-AI Alignment](https://mlsanity.com/paper/2412.15114/) (2024)
- [Split Happens: Combating Advanced Threats with Split Learning and Function Secret Sharing](https://mlsanity.com/paper/2507.10494/) (2025)
- [Oops!... They Stole it Again: Attacks on Split Learning](https://mlsanity.com/paper/2508.10598/) (2025)
- [A Privacy-Centric Approach: Scalable and Secure Federated Learning Enabled by Hybrid Homomorphic Encryption](https://mlsanity.com/paper/2507.14853/) (2025)
- [FHE-Agent: Automating CKKS Configuration for Practical Encrypted Inference via an LLM-Guided Agentic Framework](https://mlsanity.com/paper/2511.18653/) (2025)
- [Fully Homomorphic Encryption on Llama 3 model for privacy preserving LLM inference](https://mlsanity.com/paper/2604.12168/) (2026)

## Abstract (arXiv)

Machine Learning (ML) has emerged as one of data science's most transformative and influential domains. However, the widespread adoption of ML introduces privacy-related concerns owing to the increasing number of malicious attacks targeting ML models. To address these concerns, Privacy-Preserving Machine Learning (PPML) methods have been introduced to safeguard the privacy and security of ML models. One such approach is the use of Homomorphic Encryption (HE). However, the significant drawbacks and inefficiencies of traditional HE render it impractical for highly scalable scenarios. Fortunately, a modern cryptographic scheme, Hybrid Homomorphic Encryption (HHE), has recently emerged, combining the strengths of symmetric cryptography and HE to surmount these challenges. Our work seeks to introduce HHE to ML by designing a PPML scheme tailored for end devices. We leverage HHE as the fundamental building block to enable secure learning of classification outcomes over encrypted data, all while preserving the privacy of the input data and ML model. We demonstrate the real-world applicability of our construction by developing and evaluating an HHE-based PPML application for classifying heart disease based on sensitive ECG data. Notably, our evaluations revealed a slight reduction in accuracy compared to inference on plaintext data. Additionally, both the analyst and end devices experience minimal communication and computation costs, underscoring the practical viability of our approach. The successful integration of HHE into PPML provides a glimpse into a more secure and privacy-conscious future for machine learning on relatively constrained end devices.