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RR: A Fault Model for Efficient TEE Replication

Baltasar Dinis,P. Druschel,Rodrigo Rodrigues

Published 2023 in Network and Distributed System Security Symposium

ABSTRACT

—Trusted Execution Environments (TEEs) ensure the confidentiality and integrity of computations in hardware. Subject to the TEE’s threat model, the hardware shields a computation from most externally induced fault behavior except crashes. As a result, a crash-fault tolerant (CFT) replication protocol should be sufficient when replicating trusted code inside TEEs. However, TEEs do not provide efficient and general means of ensuring the freshness of external, persistent state. Therefore, CFT replication is insufficient for TEE computations with external state, as this state could be rolled back to an earlier version when a TEE restarts. Furthermore, using BFT protocols in this setting is too conservative, because these protocols are designed to tolerate arbitrary behavior, not just rollback during a restart. In this paper, we propose the restart-rollback (RR) fault model for replicating TEEs, which precisely captures the possible fault behaviors of TEEs with external state. Then, we show that existing replication protocols can be easily adapted to this fault model with few changes, while retaining their original performance. We adapted two widely used crash fault tolerant protocols — the ABD [6] read/write register protocol and the Paxos [34] consensus protocol — to the RR model. Furthermore, we leverage these protocols to build a replicated metadata service called TEEMS , and then show that it can be used to add TEE-grade confidentiality, integrity, and freshness to untrusted cloud storage services. Our evaluation shows that our protocols perform significantly better than their BFT counterparts (between 1 . 25 and 55 × better throughput), while performing identically to the CFT versions, which do not protect against rollback attacks.

PUBLICATION RECORD

  • Publication year

    2023

  • Venue

    Network and Distributed System Security Symposium

  • Publication date

    Unknown publication date

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.14722/ndss.2023.24001
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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