Hopper：面向拜占庭容错对等采样的有界内存协作去偏
Hopper: Bounded-Memory Collaborative Debiasing for Byzantine-Tolerant Peer Sampling
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- Univ. Bordeaux, CNRS, Bordeaux INP, LaBRI, UMR 5800（波尔多大学、法国国家科学研究中心、波尔多国立高等电子与信息技术学院、计算机研究实验室）
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中文总结 AI 辅助
Hopper提出有界内存去偏协议，通过流估计器BitMatcher和饱和触发衰减机制BMDecay，应对延迟平衡攻击，实现拜占庭容错对等采样的快速恢复与可信协作。
中文摘要 AI 辅助
拜占庭容错的对等采样依赖于持续刷新的视图，然而对手可以偏置用于构造这些视图的标识符流。频率感知的去偏方法会对过度表示的标识符进行降权，但现有设计依赖于累计的每标识符计数。我们证明，即使是精确的无界计数器，在延迟平衡攻击下也会失效，这种攻击中，一段较长的良性前缀掩盖了随后出现的对抗性频率偏移。我们提出了Hopper，一种用于拜占庭容错对等采样的有界内存去偏协议。我们识别了去偏所需的流估计属性，并在评估的备选方案中选择了BitMatcher作为最能保留对抗性频率结构的估计器。Hopper添加了BMDecay，一种饱和触发的衰减与重建机制，可在长时间执行中保持该信号的时效性。Hopper还通过认证的指纹感知重建和角色特定去偏来支持可信协作。实验表明，与仅依赖BitMatcher相比，Hopper能更快地从延迟攻击中恢复，并且在固定内存预算下，其去偏效果与不去偏的基线相当。可信协作降低了攻击后的污染峰值，但在高可信节点密度下产生了重识别权衡。这些结果显示了出现频率的新鲜度（而非仅精确计数）作为实用频率感知拜占庭对等采样的关键要求的重要性。
英文摘要
Byzantine-tolerant peer sampling relies on continuously refreshed views, yet an adversary can bias the identifier streams used to construct them. Frequency-aware debiasing downweights overrepresented identifiers, but existing designs rely on cumulative per-identifier counts. We show that even exact, unbounded counters fail under a delayed balanced attack, in which a long benign prefix masks a subsequent adversarial frequency shift. We introduce Hopper, a bounded-memory debiasing protocol for Byzantine-tolerant peer sampling. We identify the stream-estimation properties required for debiasing and select BitMatcher as the estimator that best preserves adversarial frequency structure among the evaluated alternatives. Hopper adds BMDecay, a saturation-triggered decay and reconstruction mechanism that keeps this signal fresh over long executions. Hopper also supports trusted collaboration through authenticated fingerprint-aware reconstruction and role-specific debiasing. Experiments show that Hopper recovers from delayed attacks faster than when relying on BitMatcher, and debiaising as well as non-debiasing baselines under a fixed memory budget. Trusted collaboration reduces post-attack pollution peaks but creates a re-identification trade-off at high trusted-node densities. These results show the importance of occurence freshness, rather than exact counting alone, as a key requirement for practical frequency-aware Byzantine peer sampling.
