Hopper: Bounded-Memory Collaborative Debiasing for Byzantine-Tolerant Peer Sampling
Researchers propose Hopper, a bounded-memory debiasing protocol that protects Byzantine-tolerant peer sampling from delayed balanced attacks defeating cumulative counters.
The paper shows that frequency-aware debiasing based on cumulative per-identifier counts fails under a delayed balanced attack, where a long benign prefix masks a subsequent adversarial frequency shift. Hopper introduces a bounded-memory debiasing protocol using BitMatcher as the stream estimator, plus BMDecay, a saturation-triggered decay and reconstruction mechanism keeping frequency signal fresh. It also supports authenticated fingerprint-aware trusted collaboration with role-specific debiasing. Experiments show faster recovery from delayed attacks and debiasing comparable to non-debiasing baselines under a fixed memory budget, with a re-identification trade-off at high trusted-node densities.
- Exact cumulative counters fail under delayed balanced attacks in peer sampling.
- Hopper combines BitMatcher estimator with BMDecay saturation-triggered decay.
- Trusted collaboration reduces post-attack pollution but creates re-identification trade-off.
- Occurrence freshness, not exact counting, is key to practical debiasing.
Full article186 words · extracted from arxiv.org · click to collapse
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.
Text extracted automatically; images, tables and formatting may be missing. Original: https://arxiv.org/abs/2609.19893