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CrudiTEE: A Stick-And-Carrot Approach to Building Trustworthy Cryptocurrency Wallets with TEEs

Authors Lulu Zhou , Zeyu Liu , Fan Zhang , Michael K. Reiter

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  • Security and privacy → Authorization
  • Security and privacy → Side-channel analysis and countermeasures
Keywords
  • Cryptocurrency wallet
  • blockchain

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Abstract

Cryptocurrency introduces usability challenges by requiring users to manage signing keys. Popular signing key management services (e.g., custodial wallets), however, either introduce a trusted party or burden users with managing signing key shares, posing the same usability challenges. TEE (Trusted Execution Environment) is a promising technology to avoid both, but practical implementations of TEEs suffer from various side-channel attacks that have proven hard to eliminate. 
This paper explores a new approach to side-channel mitigation through economic incentives for TEE-based cryptocurrency wallet solutions. By taking the cost and profit of side-channel attacks into consideration, we designed a Stick-and-Carrot-based cryptocurrency wallet, CrudiTEE, that leverages penalties (the stick) and rewards (the carrot) to disincentivize attackers from exfiltrating signing keys in the first place. We model the attacker’s behavior using a Markov Decision Process (MDP) to evaluate the effectiveness of the bounty and enable the service provider to adjust the parameters of the bounty’s reward function accordingly.

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Lulu Zhou, Zeyu Liu, Fan Zhang, and Michael K. Reiter. CrudiTEE: A Stick-And-Carrot Approach to Building Trustworthy Cryptocurrency Wallets with TEEs. In 6th Conference on Advances in Financial Technologies (AFT 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 316, pp. 16:1-16:25, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.AFT.2024.16

Author Details

Lulu Zhou
  • Yale University, New Haven, CT, USA
Zeyu Liu
  • Yale University, New Haven, CT, USA
Fan Zhang
  • Yale University, New Haven, CT, USA
Michael K. Reiter
  • Duke University, New Haven, CT, USA

Funding

This work was funded in part by NSF grant 2207214 and an Ethereum Academic Grant.

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References

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