Vigil: Accountable Liveness against Selective Silence

📅 2026-09-16
📈 Citations: 0
Influential: 0
📄 PDF
🤖 AI Summary
本文针对选择性沉默问题,提出了一种名为Vigil的新方法,通过攻击自适应转发、位图交叉认证等技术来解决拜占庭容错系统中的活性违规问题。
📝 Abstract
BFT accountability is well understood for safety violations, and recent work attributes global liveness violations; \emph{recipient-selective} silence remains unresolved. A selectively silent adversary withholds messages from some honest nodes while behaving correctly toward others. It can stall consensus yet evade every existing mechanism. We initiate a systematic study of accountability against selective silence. Negatively, a lone attacker silent toward at most $f$ honest nodes is indistinguishable from an honest node, yielding a universal lower bound $K_{\mathrm{SI}} \ge f{+}1$ on the \emph{silence identification threshold}; moreover, any feedback-free repair after a silence-induced violation costs $Θ(n^3)$. Positively, \textsc{Vigil}, a Tendermint variant, matches these bounds with attack-adaptive forwarding, via bitmap cross-attestation, core-based membership, and challenge--response auditing. It pays $O(n)$ authenticators per node when no selective silence occurs (plus $Θ(n^2)$ bitmap metadata bits per node), relays in proportion to the attack's width (sub-threshold silence can force up to $n^3/27$ relays per view, a cost we price exactly), and majority-accuses any node silent toward more than a tunable resilience $τ_A$ of honest peers ($K_{\mathrm{SI}} = τ_A{+}1$, optimal at $τ_A = f$). We also price the residual sub-threshold griefing surface exactly and extend identification to $x$-partial synchrony. Real-network experiments on a three-region WAN, together with a simulator held to exact equality with every closed form, confirm each threshold and cost: at $2\%$ loss, an $f{+}1$ accusation bar falsely accuses $91.2\%$ of honest nodes, while our majority bar accuses $0.002\%$.
Problem

Research questions and friction points this paper is trying to address.

selective silence
BFT accountability
liveness violation
Innovation

Methods, ideas, or system contributions that make the work stand out.

selective silence
accountability
Tendermint variant
bitmap cross-attestation
challenge-response auditing
🔎 Similar Papers
No similar papers found.
💼 Related Jobs
No related jobs found.
J
Jiawei Cheng
School of Software & Microelectronics, Peking University, Beijing, China
Huiping Sun
Huiping Sun
School of Software & Microelectronics, Peking University, Beijing, China
R
Rui Zhou
School of Software & Microelectronics, Peking University, Beijing, China
J
Jinjue Zhou
School of Software & Microelectronics, Peking University, Beijing, China
Zhong Chen
Zhong Chen
School of Computing, Southern Illinois University
Machine LearningDeep LearningLarge Language ModelsAI for HealthAI for Science and Education