# A Survey of Secure Computation Using Trusted Execution Environments

arXiv: https://arxiv.org/abs/2302.12150 · HTML: https://mlsanity.com/paper/2302.12150/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 165 references that are in the map.
- First posted on arXiv 23 Feb 2023.

## Builds on (most cited first, 30 of 31)

- [Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware](https://mlsanity.com/paper/1806.03287/) (2018)
- [PPFL: Privacy-preserving Federated Learning with Trusted Execution Environments](https://mlsanity.com/paper/2104.14380/) (2021)
- [FALCON: Honest-Majority Maliciously Secure Framework for Private Deep Learning](https://mlsanity.com/paper/2004.02229/) (2020)
- [CrypTFlow: Secure TensorFlow Inference](https://mlsanity.com/paper/1909.07814/) (2019)
- [DarkneTZ: Towards Model Privacy at the Edge using Trusted Execution Environments](https://mlsanity.com/paper/2004.05703/) (2020)
- [Chiron: Privacy-preserving Machine Learning as a Service](https://mlsanity.com/paper/1803.05961/) (2018)
- [MLCapsule: Guarded Offline Deployment of Machine Learning as a Service](https://mlsanity.com/paper/1808.00590/) (2018)
- [Privacy-Preserving Inference in Machine Learning Services Using Trusted Execution Environments](https://mlsanity.com/paper/1912.03485/) (2019)
- [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)
- [secureTF: A Secure TensorFlow Framework](https://mlsanity.com/paper/2101.08204/) (2021)
- [3LegRace: Privacy-Preserving DNN Training over TEEs and GPUs](https://mlsanity.com/paper/2110.01229/) (2021)
- [TensorSCONE: A Secure TensorFlow Framework using Intel SGX](https://mlsanity.com/paper/1902.04413/) (2019)
- [A Survey of Coded Distributed Computing](https://mlsanity.com/paper/2008.09048/) (2020)
- [Conclave: secure multi-party computation on big data (extended TR)](https://mlsanity.com/paper/1902.06288/) (2019)
- [A Hybrid Approach to Secure Function Evaluation Using SGX](https://mlsanity.com/paper/1905.01233/) (2019)
- [SMCQL: Secure Querying for Federated Databases](https://mlsanity.com/paper/1606.06808/) (2016)
- [An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors](https://mlsanity.com/paper/1807.00736/) (2018)
- [Confidential Inference via Ternary Model Partitioning](https://mlsanity.com/paper/1807.00969/) (2018)
- [Shrinkwrap: Differentially-Private Query Processing in Private Data Federations](https://mlsanity.com/paper/1810.01816/) (2018)
- [Confidential Machine Learning Computation in Untrusted Environments: A Systems Security Perspective](https://mlsanity.com/paper/2111.03308/) (2021)
- [Plinius: Secure and Persistent Machine Learning Model Training](https://mlsanity.com/paper/2104.02987/) (2021)
- [HardIDX: Practical and Secure Index with SGX](https://mlsanity.com/paper/1703.04583/) (2017)
- [LightBox: Full-stack Protected Stateful Middlebox at Lightning Speed](https://mlsanity.com/paper/1706.06261/) (2017)
- [StealthDB: a Scalable Encrypted Database with Full SQL Query Support](https://mlsanity.com/paper/1711.02279/) (2017)
- [Oblivious Sampling Algorithms for Private Data Analysis](https://mlsanity.com/paper/2009.13689/) (2020)
- [PCT-TEE: Trajectory-based Private Contact Tracing System with Trusted Execution Environment](https://mlsanity.com/paper/2012.03782/) (2020)
- [The Circle Game: Scalable Private Membership Test Using Trusted Hardware](https://mlsanity.com/paper/1606.01655/) (2016)
- [The bumpy road towards iPhone 5c NAND mirroring](https://mlsanity.com/paper/1609.04327/) (2016)
- [Efficient Oblivious Database Joins](https://mlsanity.com/paper/2003.09481/) (2020)

## Built on by (most cited first, 8 of 8)

- [Is Your LLM Overcharging You? Tokenization, Transparency, and Incentives](https://mlsanity.com/paper/2505.21627/) (2025)
- [Spore in the Wild: A Case Study of Spore.fun as an Open-Environment Evolution Experiment with Sovereign AI Agents on TEE-Secured Blockchains](https://mlsanity.com/paper/2506.04236/) (2025)
- [Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training](https://mlsanity.com/paper/2401.11531/) (2024)
- [Privacy-Preserving and Trustworthy Deep Learning for Medical Imaging](https://mlsanity.com/paper/2407.00538/) (2024)
- [Provably Secure Retrieval-Augmented Generation](https://mlsanity.com/paper/2508.01084/) (2025)
- [PrivSpike: Employing Homomorphic Encryption for Private Inference of Deep Spiking Neural Networks](https://mlsanity.com/paper/2510.03995/) (2025)
- [Selective Token-Level Cryptographic Redaction for Privacy-Preserving Clinical Deployment of Large Language Models](https://mlsanity.com/paper/2606.03399/) (2026)
- [RT-SHCUA: Real-Time Self-Hosted Computer-Use Agent for UAV Control](https://mlsanity.com/paper/2607.17951/) (2026)

## Abstract (arXiv)

As an essential technology underpinning trusted computing, the trusted execution environment (TEE) allows one to launch computation tasks on both on- and off-premises data while assuring confidentiality and integrity. This article provides a systematic review and comparison of TEE-based secure computation protocols. We first propose a taxonomy that classifies secure computation protocols into three major categories, namely secure outsourced computation, secure distributed computation and secure multi-party computation. To enable a fair comparison of these protocols, we also present comprehensive assessment criteria with respect to four aspects: setting, methodology, security and performance. Based on these criteria, we review, discuss and compare the state-of-the-art TEE-based secure computation protocols for both general-purpose computation functions and special-purpose ones, such as privacy-preserving machine learning and encrypted database queries. To the best of our knowledge, this article is the first survey to review TEE-based secure computation protocols and the comprehensive comparison can serve as a guideline for selecting suitable protocols for deployment in practice. Finally, we also discuss several future research directions and challenges.