Distributed ledgers are increasingly relied upon by industry to provide trustworthy accountability, strong integrity protection, and high availability for critical data without centralizing trust. Recently, distributed append-only logs are opting for a layered approach, combining crash-fault-tolerant (CFT) consensus with hardware-based Trusted Execution Environments (TEEs) for greater resiliency. Unfortunately, hardware TEEs can be subject to (rare) attacks, undermining the very guarantees that distributed ledgers are carefully designed to achieve. In response, we present Pirateship, a new distributed consensus protocol that cautiously trusts the guarantees of TEEs. Pirateship carefully embeds a Byzantine fault-tolerant (BFT) protocol inside of a CFT protocol with no additional messages. This is made possible through careful refactoring of both the CFT and BFT protocols such that their structure aligns. Pirateship achieves performance in line with regular TEE-enabled consensus protocols, while guaranteeing integrity in the face of TEE platform compromises.

Time-travel queries ask what data looked like at a past time or over an interval. Modern databases support time-travel queries, but this support is insufficient for applications that cannot trust the query server, such as secure messaging and decentralized applications, where clients need proofs that the returned results are correct. Existing approaches either require aggregating evidence from multiple auditors, incur per-step proof costs that scale linearly with history length, or impose massive storage and communication costs. In this paper, we introduce Smaran, an authenticated data structure that enables servers to answer time-travel queries efficiently with a constant number of fixed-size proofs, independent of the query range. Smaran achieves this through a novel integration of segment trees with vector commitments, sidestepping scalability challenges as history grows via a hierarchical architecture, and avoiding redundancy by introducing versioned vector commitments.

Trusted Execution Environments (TEEs) provide strong confidentiality and integrity guarantees to distributed data processing. Developers are increasingly using clusters of TEE-enabled workers for these applications. However, TEEs do not protect persistent state: a rollback attack may replace the current persistent state with a stale version. Conventional rollback detection systems interfere with the application’s steady-state performance either by adding high coordination overhead or log amplification. Our key insight is 'Rollback attacks violate causality'. Rollback attacks can be efficiently detected by causal logging, avoiding the coordination and log amplification overheads of prior solutions. We build HarborMaster, a high-performance rollback detection system for the TEE-based distributed computing clusters. HarborMaster offloads the job of rollback detection to a specialized auditing service that efficiently checks for rollback attacks using violations of causality in the TEE-based distributed computing workers. Running in AMD SEV-SNP Confidential VMs, HarborMaster only imposes 8–35% logging overhead and preserves the linear scaling properties of an unprotected distributed application.

Today's practical Byzantine Fault Tolerant (BFT) state machine replication deployments are vulnerable to slowdowns. The main culprit is timeouts. Aggressive timeouts spuriously trigger expensive leader changes, while conservative timeouts leave the system idle and let slowdowns severely inflate latency. Two main alternatives exist: hedging, which improves recovery from slow leaders but still incurs a time-based hedging delay, and cooperative asynchronous protocols, which recover quickly from slowdowns but suffer from high common-case latency and low throughput. This paper presents Ambulance: a BFT state machine replication protocol that sidesteps this trade-off through protocol-rigged races, where replicas, rather than race against the clock, race against each other by executing protocol steps. This enables Ambulance to achieve high throughput and low latency comparable to state-of-the-art timeout-based BFT, while matching the robustness of cooperative approaches.

Threshold Fully Homomorphic Encryption (ThFHE) enables arbitrary computation over encrypted data while keeping the decryption key distributed across multiple parties at all times. ThFHE is a key enabler for threshold cryptography and, more generally, secure distributed computing. Existing ThFHE schemes relying on standard hardness assumptions, inherently require highly inefficient parameters and are unsuitable for practical deployment. In this paper, we take a novel approach towards making ThFHE practically usable by (i) proposing an efficient ThFHE scheme with a new analysis resulting in significantly improved parameters; (ii) and providing the first practical ThFHE implementation benchmark based on Torus FHE. We propose the first practical ThFHE scheme with a polynomial modulus-to-noise ratio that supports practically efficient parameters while retaining provable security based on standard quantum-safe assumptions. We achieve this via Renyi divergence-based security analysis of our proposed threshold decryption mechanism. We present a prototype software implementation of our proposed ThFHE scheme that builds upon the existing Torus-FHE library and supports (distributed) decryption on highly resource-constrained ARM-based handheld devices. Along the way, we implement several extensions to the Torus FHE library, including a Torus-based linear integer secret sharing subroutine to support ThFHE key sharing and distributed decryption for any threshold access structure. We illustrate the efficacy of our proposal via an end-to-end use case involving encrypted computations over a real medical database and distributed decryptions of the computed result on resource-constrained ARM-based handheld devices.