# Additively Homomorphical Encryption based Deep Neural Network for Asymmetrically Collaborative Machine Learning

arXiv: https://arxiv.org/abs/2007.06849 · HTML: https://mlsanity.com/paper/2007.06849/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 19 of 19)

- [Vertical Federated Learning: Concepts, Advances and Challenges](https://mlsanity.com/paper/2211.12814/) (2022)
- [BlindFL: Vertical Federated Machine Learning without Peeking into Your Data](https://mlsanity.com/paper/2206.07975/) (2022)
- [A Survey on Vertical Federated Learning: From a Layered Perspective](https://mlsanity.com/paper/2304.01829/) (2023)
- [Privacy-preserving Federated Adversarial Domain Adaption over Feature Groups for Interpretability](https://mlsanity.com/paper/2111.10934/) (2021)
- [A Survey of Trustworthy Federated Learning with Perspectives on Security, Robustness, and Privacy](https://mlsanity.com/paper/2302.10637/) (2023)
- [FedSDG-FS: Efficient and Secure Feature Selection for Vertical Federated Learning](https://mlsanity.com/paper/2302.10417/) (2023)
- [Residue-based Label Protection Mechanisms in Vertical Logistic Regression](https://mlsanity.com/paper/2205.04166/) (2022)
- [SecFormer: Fast and Accurate Privacy-Preserving Inference for Transformer Models via SMPC](https://mlsanity.com/paper/2401.00793/) (2024)
- [A Survey of Privacy Threats and Defense in Vertical Federated Learning: From Model Life Cycle Perspective](https://mlsanity.com/paper/2402.03688/) (2024)
- [FedAds: A Benchmark for Privacy-Preserving CVR Estimation with Vertical Federated Learning](https://mlsanity.com/paper/2305.08328/) (2023)
- [EFMVFL: An Efficient and Flexible Multi-party Vertical Federated Learning without a Third Party](https://mlsanity.com/paper/2201.06244/) (2022)
- [Secure Vertical Federated Learning Under Unreliable Connectivity](https://mlsanity.com/paper/2305.16794/) (2023)
- [UniFed: All-In-One Federated Learning Platform to Unify Open-Source Frameworks](https://mlsanity.com/paper/2207.10308/) (2022)
- [Efficient Vertical Federated Learning with Secure Aggregation](https://mlsanity.com/paper/2305.11236/) (2023)
- [SecureBoost+: Large Scale and High-Performance Vertical Federated Gradient Boosting Decision Tree](https://mlsanity.com/paper/2110.10927/) (2021)
- [FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation](https://mlsanity.com/paper/2312.00102/) (2023)
- [FedHGN: A Federated Framework for Heterogeneous Graph Neural Networks](https://mlsanity.com/paper/2305.09729/) (2023)
- [PackVFL: Efficient HE Packing for Vertical Federated Learning](https://mlsanity.com/paper/2405.00482/) (2024)
- [VMask: Tunable Label Privacy Protection for Vertical Federated Learning via Layer Masking](https://mlsanity.com/paper/2507.14629/) (2025)

## Abstract (arXiv)

The financial sector presents many opportunities to apply various machine learning techniques. Centralized machine learning creates a constraint which limits further applications in finance sectors. Data privacy is a fundamental challenge for a variety of finance and insurance applications that account on learning a model across different sections. In this paper, we define a new practical scheme of collaborative machine learning that one party owns data, but another party owns labels only, and term this Asymmetrically Collaborative Machine Learning. For this scheme, we propose a novel privacy-preserving architecture where two parties can collaboratively train a deep learning model efficiently while preserving the privacy of each party's data. More specifically, we decompose the forward propagation and backpropagation of the neural network into four different steps and propose a novel protocol to handle information leakage in these steps. Our extensive experiments on different datasets demonstrate not only stable training without accuracy loss, but also more than 100 times speedup compared with the state-of-the-art system.