# Efficient Deep Learning on Multi-Source Private Data

arXiv: https://arxiv.org/abs/1807.06689 · HTML: https://mlsanity.com/paper/1807.06689/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jul 2018.

## Built on by (most cited first, 17 of 17)

- [Opacus: User-Friendly Differential Privacy Library in PyTorch](https://mlsanity.com/paper/2109.12298/) (2021)
- [Privacy and Fairness in Federated Learning: on the Perspective of Trade-off](https://mlsanity.com/paper/2306.14123/) (2023)
- [Machine Learning with Confidential Computing: A Systematization of Knowledge](https://mlsanity.com/paper/2208.10134/) (2022)
- [Proof of Unlearning: Definitions and Instantiation](https://mlsanity.com/paper/2210.11334/) (2022)
- [Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX](https://mlsanity.com/paper/2105.01281/) (2021)
- [DarKnight: An Accelerated Framework for Privacy and Integrity Preserving Deep Learning Using Trusted Hardware](https://mlsanity.com/paper/2207.00083/) (2022)
- [Decentralized Stochastic Optimization with Inherent Privacy Protection](https://mlsanity.com/paper/2205.03884/) (2022)
- [Blockchain-based Federated Learning with Secure Aggregation in Trusted Execution Environment for Internet-of-Things](https://mlsanity.com/paper/2304.12889/) (2023)
- [Separation of Powers in Federated Learning](https://mlsanity.com/paper/2105.09400/) (2021)
- [Quantization enabled Privacy Protection in Decentralized Stochastic Optimization](https://mlsanity.com/paper/2208.04845/) (2022)
- [Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective](https://mlsanity.com/paper/2111.03308/) (2021)
- [Proof-of-Guardrail in AI Agents and What (Not) to Trust from It](https://mlsanity.com/paper/2603.05786/) (2026)
- [Plinius: Secure and Persistent Machine Learning Model Training](https://mlsanity.com/paper/2104.02987/) (2021)
- [Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning](https://mlsanity.com/paper/2211.01346/) (2022)
- [Privacy and Integrity Preserving Training Using Trusted Hardware](https://mlsanity.com/paper/2105.00334/) (2021)
- [Secure Machine Learning in the Cloud Using One Way Scrambling by Deconvolution](https://mlsanity.com/paper/2111.03125/) (2021)
- [Protecting Confidentiality, Privacy and Integrity in Collaborative Learning](https://mlsanity.com/paper/2412.08534/) (2024)

## Abstract (arXiv)

Machine learning models benefit from large and diverse datasets. Using such datasets, however, often requires trusting a centralized data aggregator. For sensitive applications like healthcare and finance this is undesirable as it could compromise patient privacy or divulge trade secrets. Recent advances in secure and privacy-preserving computation, including trusted hardware enclaves and differential privacy, offer a way for mutually distrusting parties to efficiently train a machine learning model without revealing the training data. In this work, we introduce Myelin, a deep learning framework which combines these privacy-preservation primitives, and use it to establish a baseline level of performance for fully private machine learning.