Hopper improves peer selection under adversarial attacks with limited memory
Hopper: Bounded-Memory Collaborative Debiasing for Byzantine-Tolerant Peer Sampling
Cryptography and SecurityDistributed, Parallel, and Cluster Computing
Summary
When computers share information, they often pick random peers to talk to, but bad actors can trick the system by pretending to be many peers. Previous methods tried counting how often each peer appeared to reduce this trickery, but that approach can be fooled if the attack comes after a long normal period. The authors developed Hopper, a new method that uses smart memory techniques to keep information fresh and is better at spotting these hidden attacks. Hopper also works better when certain trusted nodes help out, though that introduces privacy trade-offs.
What this means in practice
- •For distributed system engineers: Maintain reliable and unbiased network views in systems facing sophisticated adversarial peer manipulations while using limited memory resources.
- •For cybersecurity teams: Detect and recover from stealthy attacks on peer-to-peer communication channels by improving debiasing of peer selection streams.
Authors
Joachim Bruneau-Queyreix, Laurent Reveillère, Augusta Mukam
Abstract
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.