Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contract Execution
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Abstract (arXiv)
Smart contracts are applications that execute on blockchains. Today they manage billions of dollars in value and motivate visionary plans for pervasive blockchain deployment. While smart contracts inherit the availability and other security assurances of blockchains, however, they are impeded by blockchains' lack of confidentiality and poor performance. We present Ekiden, a system that addresses these critical gaps by combining blockchains with Trusted Execution Environments (TEEs). Ekiden leverages a novel architecture that separates consensus from execution, enabling efficient TEE-backed confidentiality-preserving smart-contracts and high scalability. Our prototype (with Tendermint as the consensus layer) achieves example performance of 600x more throughput and 400x less latency at 1000x less cost than the Ethereum mainnet. Another contribution of this paper is that we systematically identify and treat the pitfalls arising from harmonizing TEEs and blockchains. Treated separately, both TEEs and blockchains provide powerful guarantees, but hybridized, though, they engender new attacks. For example, in naive designs, privacy in TEE-backed contracts can be jeopardized by forgery of blocks, a seemingly unrelated attack vector. We believe the insights learned from Ekiden will prove to be of broad importance in hybridized TEE-blockchain systems.
Key facts. Cited by 7 papers in the map (data through 14 Oct 2026). First posted on arXiv 14 Apr 2018.
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Citing papers per year: 2021: 2; 2022: 1; 2023: 1; 2024: 1; 2025: 2.
7 papers.
- 2021A Decentralized Approach towards Responsible AI in Social Ecosystemscited by 3
- 2021Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approachcited by 2
- 2023Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuitscited by 2
- 2025Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learningcited by 2
- 2025Dstack: A Zero Trust Framework for Confidential Containerscited by 2
- 2022Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learningcited by 1
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computingcited by 0
- 2025Dstack: A Zero Trust Framework for Confidential Containers15 Sep 2025
- 2025Towards Secure Program Partitioning for Smart Contracts with LLM's In-Context Learning20 Feb 2025
- 2024TensorTEE: Unifying Heterogeneous TEE Granularity for Efficient Secure Collaborative Tensor Computing12 Jul 2024
- 2023Dash: Accelerating Distributed Private Convolutional Neural Network Inference with Arithmetic Garbled Circuits13 Feb 2023
- 2022Predictive Crypto-Asset Automated Market Making Architecture for Decentralized Finance using Deep Reinforcement Learning28 Sep 2022
- 2021Federated Learning using Smart Contracts on Blockchains, based on Reward Driven Approach19 Jul 2021
- 2021A Decentralized Approach towards Responsible AI in Social Ecosystems12 Feb 2021