Arnon Amir, Michael Lindenbaum
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Sybil attack occurs when an adversary controls multiple system identifiers (IDs). Limiting the number of Sybil (bad) IDs to a minority is critical for tolerating malicious behavior. A popular tool for enforcing a bad minority is resource burning (RB): the verifiable consumption of a network resource. Unfortunately, typical RB defenses require non-Sybil (good) IDs to consume at least as many resources as the adversary. We present a new defense, ERGO, that guarantees (1) there is always a bad minority; and (2) during a significant attack, the good IDs consume asymptotically less resources than the bad. Specifically, despite high churn, the good-ID RB rate is O(TJ+J), where T is the adversary's RB rate, and J is the good-ID join rate. We show this RB rate is asymptotically optimal for a large class of algorithms, and we empirically demonstrate the benefits of ERGO.
Arnon Amir, Michael Lindenbaum
IEEE Transactions on Pattern Analysis and Machine Intelligence
William Hinsberg, Joy Cheng, et al.
SPIE Advanced Lithography 2010
Ehud Altman, Kenneth R. Brown, et al.
PRX Quantum
John S. Lew
Mathematical Biosciences