Baltasar Dinis (Instituto Superior Técnico (IST-ULisboa) / INESC-ID / MPI-SWS), Peter Druschel (MPI-SWS), Rodrigo Rodrigues (Instituto Superior Técnico (IST-ULisboa) / INESC-ID)

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 read/write register protocol and the Paxos consensus protocol - to the RR model. Furthermore, we leverage these protocols to build a replicated metadata service called emph{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 $55times$ better throughput), while performing identically to the CFT versions, which do not protect against rollback attacks.

View More Papers

Paralyzing Drones via EMI Signal Injection on Sensory Communication...

Joonha Jang (KAIST), ManGi Cho (KAIST), Jaehoon Kim (KAIST), Dongkwan Kim (Samsung SDS), Yongdae Kim (KAIST)

Read More

Analysing Adversarial Threats to Rule-Based Local-Planning Algorithms for Autonomous...

Andrew Roberts (Tallinn University of Technology), Mohsen Malayjerdi (Tallinn University of Technology), Mauro Bellone (Tallinn University of Technology), Olaf Maennel (The University of Adelaide), Ehsan Malayjerdi (Tallinn University of Technology)

Read More

WIP: The Feasibility of High-performance Message Authentication in Automotive...

Evan Allen (Virginia Tech), Zeb Bowden (Virginia Tech Transportation Institute), Randy Marchany (Virginia Tech), J. Scot Ransbottom (Virginia Tech)

Read More