Ekiden: A Platform for Confidentiality-Preserving, Trustworthy, and Performant Smart Contracts

By Raymond Cheng, Fan Zhang, Jernej Kos, Warren He, Nicholas Hynes, Noah M. Johnson, Ari Juels, Andrew K. Miller, Dawn Song. 2018

428 citations recorded by the source corpus.

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 naïve 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.

Subfields: Artificial Intelligence, Information Systems, ML Systems & Hardware, Privacy & Security

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Information Systems

In ML Systems & Hardware

In Privacy & Security

Citations in the corpus

Advances and Open Problems in Federated Learning (2019, background): “Another way of achieving secure aggregation is to use confidential smart contract such as what is enabled by the Oasis Protocol [104] which runs inside secure enclaves.”
Slalom: Fast, Verifiable and Private Execution of Neural Networks in Trusted Hardware (2018, methodology): “Yet, SGX’s security comes at a performance cost, and there remains a large gap between TEEs and untrusted devices.”
Biscotti: A Ledger for Private and Secure Peer-to-Peer Machine Learning (2018, background): “recently proposed the use of TEEs and privacy-preserving smart contracts to provide security and privacy to multi-party ML tasks [16].”
Survey on blockchain based smart contracts: Applications, opportunities and challenges (2021, background): “Cheng et al. [223] a new transaction framework considering in the existing energy market.”
The Roadmap to 6G Security and Privacy (2021, background): “The additional privacy preservation mechanisms such as privacy by design [110], [111] and TEE [112], [113] can be integrating to prevent privacy leakages in blockchain based 6G services [114], [115].”
Biscotti: A Blockchain System for Private and Secure Federated Learning (2021, background): “party party computation [88], [89] or trusted execution environments [36] to encrypt the model parameters and the training data when performing multi-party ML.”
Zether: Towards Privacy in a Smart Contract World (2020, methodology): “This stronger kind of privacy is provided by some recent proposals like Hawk [46] and Ekiden [24], but they are not fully decentralized.”
Arbitrum: Scalable, private smart contracts (2018, methodology): “Ekiden [13] uses a TEE to achieve scalable and private smart contracts.”
Security and Privacy on Blockchain (2019, background): “For example, Ekiden [29] is a SGX based solution for confidentiality-preserving smart contracts.”