# Integrating Homomorphic Encryption and Trusted Execution Technology for Autonomous and Confidential Model Refining in Cloud

arXiv: https://arxiv.org/abs/2308.00963 · HTML: https://mlsanity.com/paper/2308.00963/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 41 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 17 of 17)

- [Gazelle: A Low Latency Framework for Secure Neural Network Inference](https://mlsanity.com/paper/1801.05507/) (2018)
- [Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware](https://mlsanity.com/paper/1806.03287/) (2018)
- [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning](https://mlsanity.com/paper/2004.02229/) (2020)
- [Chameleon: A Hybrid Secure Computation Framework for Machine Learning Applications](https://mlsanity.com/paper/1801.03239/) (2018)
- [Privacy-Preserving Machine Learning with Fully Homomorphic Encryption for Deep Neural Network](https://mlsanity.com/paper/2106.07229/) (2021)
- [XONN: XNOR-based Oblivious Deep Neural Network Inference](https://mlsanity.com/paper/1902.07342/) (2019)
- [DeepSecure: Scalable Provably-Secure Deep Learning](https://mlsanity.com/paper/1705.08963/) (2017)
- [Faster CryptoNets: Leveraging Sparsity for Real-World Encrypted Inference](https://mlsanity.com/paper/1811.09953/) (2018)
- [BLAZE: Blazing Fast Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2005.09042/) (2020)
- [Trident: Efficient 4PC Framework for Privacy Preserving Machine Learning](https://mlsanity.com/paper/1912.02631/) (2019)
- [SWIFT: Super-fast and Robust Privacy-Preserving Machine Learning](https://mlsanity.com/paper/2005.10296/) (2020)
- [Leaky Cauldron on the Dark Land: Understanding Memory Side-Channel Hazards in SGX](https://mlsanity.com/paper/1705.07289/) (2017)
- [SgxPectre Attacks: Stealing Intel Secrets from SGX Enclaves via Speculative Execution](https://mlsanity.com/paper/1802.09085/) (2018)
- [Citadel: Protecting Data Privacy and Model Confidentiality for Collaborative Learning with SGX](https://mlsanity.com/paper/2105.01281/) (2021)
- [BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference](https://mlsanity.com/paper/1906.00639/) (2019)
- [Privacy-preserving Cloud-based DNN Inference](https://mlsanity.com/paper/2102.03915/) (2021)
- [Towards Practical Privacy-Preserving Solution for Outsourced Neural Network Inference](https://mlsanity.com/paper/2206.02384/) (2022)

## Abstract (arXiv)

With the popularity of cloud computing and machine learning, it has been a trend to outsource machine learning processes (including model training and model-based inference) to cloud. By the outsourcing, other than utilizing the extensive and scalable resource offered by the cloud service provider, it will also be attractive to users if the cloud servers can manage the machine learning processes autonomously on behalf of the users. Such a feature will be especially salient when the machine learning is expected to be a long-term continuous process and the users are not always available to participate. Due to security and privacy concerns, it is also desired that the autonomous learning preserves the confidentiality of users' data and models involved. Hence, in this paper, we aim to design a scheme that enables autonomous and confidential model refining in cloud. Homomorphic encryption and trusted execution environment technology can protect confidentiality for autonomous computation, but each of them has their limitations respectively and they are complementary to each other. Therefore, we further propose to integrate these two techniques in the design of the model refining scheme. Through implementation and experiments, we evaluate the feasibility of our proposed scheme. The results indicate that, with our proposed scheme the cloud server can autonomously refine an encrypted model with newly provided encrypted training data to continuously improve its accuracy. Though the efficiency is still significantly lower than the baseline scheme that refines plaintext-model with plaintext-data, we expect that it can be improved by fully utilizing the higher level of parallelism and the computational power of GPU at the cloud server.