Accountable Liveness

• URL: http://arxiv.org/abs/2504.12218v2
• Date: Thu, 11 Sep 2025 04:16:44 GMT
• Title: Accountable Liveness
• Authors: Andrew Lewis-Pye, Joachim Neu, Tim Roughgarden, Luca Zanolini,
• Abstract summary: We study what analogous accountability guarantees are achievable for liveness.<n>We prove a precise characterization of the parameter regime in which accountable liveness is achievable.
• Score: 9.474815797030628
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Safety and liveness are the two classical security properties of consensus protocols. Recent works have strengthened safety with accountability: should any safety violation occur, a sizable fraction of adversary nodes can be proven to be protocol violators. This paper studies to what extent analogous accountability guarantees are achievable for liveness. To reveal the full complexity of this question, we introduce an interpolation between the classical synchronous and partially-synchronous models that we call the $x$-partially-synchronous network model in which, intuitively, at most an $x$ fraction of the time steps in any sufficiently long interval are asynchronous (and, as with a partially-synchronous network, all time steps are synchronous following the passage of an unknown "global stablization time"). We prove a precise characterization of the parameter regime in which accountable liveness is achievable: if and only if $x < 1/2$ and $f < n/2$, where $n$ denotes the number of nodes and $f$ the number of nodes controlled by an adversary. We further refine the problem statement and our analysis by parameterizing by the number of violating nodes identified following a liveness violation, and provide evidence that the guarantees achieved by our protocol are near-optimal (as a function of $x$ and $f$). Our results provide rigorous foundations for liveness-accountability heuristics such as the "inactivity leaks" employed in Ethereum.

Related papers

• RAIN: Secure and Robust Aggregation under Shuffle Model of Differential Privacy [46.52109845749167]
  We present Robust Aggregation in Noise (RAIN), a framework that reconciles privacy, robustness, and verifiability under Shuffle-DP.<n>RAIN adopts sign-space aggregation to robustly measure update consistency and limit malicious influence under noise and anonymization.<n>We show that RAIN maintains strong privacy guarantees under Shuffle-DP and remains robust to poisoning attacks with negligible degradation in accuracy and convergence.
  arXiv  Detail & Related papers  (2026-03-03T15:41:54Z)
• Do We Need Asynchronous SGD? On the Near-Optimality of Synchronous Methods [59.72933231179977]
  We revisit Synchronous SGD and its robust variant, called $m$-Synchronous SGD, and theoretically show that they are nearly optimal in many heterogeneous computation scenarios.<n>While synchronous methods are not universal solutions and there exist tasks where asynchronous methods may be necessary, we show that they are sufficient for many modern heterogeneous computation scenarios.
  arXiv  Detail & Related papers  (2026-02-03T18:02:14Z)
• Time Is All It Takes: Spike-Retiming Attacks on Event-Driven Spiking Neural Networks [87.16809558673403]
  Spiking neural networks (SNNs) compute with discrete spikes and exploit temporal structure.<n>We study a timing-only adversary that retimes existing spikes while preserving spike counts and amplitudes in event-driven SNNs.
  arXiv  Detail & Related papers  (2026-02-03T09:06:53Z)
• RealSec-bench: A Benchmark for Evaluating Secure Code Generation in Real-World Repositories [58.32028251925354]
  Large Language Models (LLMs) have demonstrated remarkable capabilities in code generation, but their proficiency in producing secure code remains a critical, under-explored area.<n>We introduce RealSec-bench, a new benchmark for secure code generation meticulously constructed from real-world, high-risk Java repositories.
  arXiv  Detail & Related papers  (2026-01-30T08:29:01Z)
• Universally Composable Termination Analysis of Tendermint [3.6181225888186055]
  This paper presents the first universally composable (UC) security analysis of Tendermint.<n>It demonstrates its resilience against strategic message-delay attacks.<n>Our main result proves that the Tendermint protocol UC-realizes the ideal Tendermint model.
  arXiv  Detail & Related papers  (2025-10-01T16:44:23Z)
• Perfectly-Private Analog Secure Aggregation in Federated Learning [51.61616734974475]
  In federated learning, multiple parties train models locally and share their parameters with a central server, which aggregates them to update a global model.<n>In this paper, a novel secure parameter aggregation method is proposed that employs the torus rather than a finite field.
  arXiv  Detail & Related papers  (2025-09-10T15:22:40Z)
• Federated Contextual Cascading Bandits with Asynchronous Communication and Heterogeneous Users [95.77678166036561]
  We propose a UCB-type algorithm with delicate communication protocols. We give sub-linear regret bounds on par with those achieved in the synchronous framework. Empirical evaluation on synthetic and real-world datasets validates our algorithm's superior performance in terms of regrets and communication costs.
  arXiv  Detail & Related papers  (2024-02-26T05:31:14Z)
• Concurrent Asynchronous Byzantine Agreement in Expected-Constant Rounds, Revisited [3.8014967401609208]
  We present the first information-theoretic multi-valued OCC protocol in the asynchronous setting with optimal resiliency. Our protocol efficiently implements with an exponential-size domain. We also provide proof in Canetti's Universal Composability framework.
  arXiv  Detail & Related papers  (2023-12-22T08:10:11Z)
• Liveness Checking of the HotStuff Protocol Family [2.07180164747172]
  Byzantine consensus protocols aim at maintaining safety guarantees under any network synchrony model. Several protocols have been shown to violate liveness properties under certain scenarios. We use temperature and lasso detection methods to check the liveness of Byzantine consensus protocols.
  arXiv  Detail & Related papers  (2023-10-13T11:03:13Z)
• Short Paper: Accountable Safety Implies Finality [10.589723476970443]
  Two key desiderata have been studied for Byzantine-fault tolerant (BFT) state-machine replication (SMR) consensus protocols. We show that accountable safety implies finality, thereby unifying earlier results.
  arXiv  Detail & Related papers  (2023-08-31T17:58:38Z)
• Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior [51.60683890503293]
  We propose a theoretical framework for studying behavior cloning of complex expert demonstrations using generative modeling. We show that pure supervised cloning can generate trajectories matching the per-time step distribution of arbitrary expert trajectories.
  arXiv  Detail & Related papers  (2023-07-27T04:27:26Z)
• Nonparametric extensions of randomized response for private confidence sets [51.75485869914048]
  This work derives methods for performing nonparametric, nonasymptotic statistical inference for population means under the constraint of local differential privacy (LDP) We present confidence intervals (CI) and time-uniform confidence sequences (CS) for $mustar$ when only given access to the privatized data.
  arXiv  Detail & Related papers  (2022-02-17T16:04:49Z)
• A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits [118.22458816174144]
  We propose a novel robust elimination-type algorithm that runs in epochs, combines exploration with infrequent switching to select a small subset of actions, and plays each action for multiple time instants. Our algorithm, GP Robust Phased Elimination (RGP-PE), successfully balances robustness to corruptions with exploration and exploitation. We perform the first empirical study of robustness in the corrupted GP bandit setting, and show that our algorithm is robust against a variety of adversarial attacks.
  arXiv  Detail & Related papers  (2022-02-03T21:19:36Z)
• LSTC: Boosting Atomic Action Detection with Long-Short-Term Context [60.60267767456306]
  We decompose the action recognition pipeline into short-term and long-term reliance. Within our design, a local aggregation branch is utilized to gather dense and informative short-term cues. Both branches independently predict the context-specific actions and the results are merged in the end.
  arXiv  Detail & Related papers  (2021-10-19T10:09:09Z)
• Contrastive Self-supervised Sequential Recommendation with Robust Augmentation [101.25762166231904]
  Sequential Recommendationdescribes a set of techniques to model dynamic user behavior in order to predict future interactions in sequential user data. Old and new issues remain, including data-sparsity and noisy data. We propose Contrastive Self-Supervised Learning for sequential Recommendation (CoSeRec)
  arXiv  Detail & Related papers  (2021-08-14T07:15:25Z)
• Safe Reinforcement Learning with Linear Function Approximation [48.75026009895308]
  We introduce safety as an unknown linear cost function of states and actions, which must always fall below a certain threshold. We then present algorithms, termed SLUCB-QVI and RSLUCB-QVI, for episodic Markov decision processes (MDPs) with linear function approximation. We show that SLUCB-QVI and RSLUCB-QVI, while with emphno safety violation, achieve a $tildemathcalOleft(kappasqrtd3H3Tright)$ regret, nearly matching
  arXiv  Detail & Related papers  (2021-06-11T08:46:57Z)
• Fully Asynchronous Policy Evaluation in Distributed Reinforcement Learning over Networks [14.636457985379746]
  This paper proposes a emphfully asynchronous scheme for the policy evaluation problem of distributed reinforcement learning (DisRL) over directed peer-to-peer networks. Without waiting for any other node of the network, each node can locally update its value function at any time by using (possibly delayed) information from its neighbors.
  arXiv  Detail & Related papers  (2020-03-01T08:12:08Z)
• Finite-Time Analysis of Asynchronous Stochastic Approximation and $Q$-Learning [12.91948651812873]
  We consider a general asynchronous Approximation scheme featuring a weighted infinity-norm contractive operator, and prove a bound on its finite-time convergence rate on a single trajectory. The resulting bound matches the sharpest available bound for synchronous $Q$-learning, and improves over previous known bounds for asynchronous $Q$-learning.
  arXiv  Detail & Related papers  (2020-02-01T19:20:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.