Bankrupting Sybil despite churn

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
Diksha Gupta , Jared Saia , Maxwell Young
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引用次数: 0

Abstract

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.

让西比尔破产,尽管搅拌
当对手控制多个系统标识符(ID)时,就会发生Sybil攻击。将Sybil(坏)ID的数量限制在少数对于容忍恶意行为至关重要。一个流行的强制执行坏少数的工具是资源燃烧(RB):网络资源的可验证消耗。不幸的是,典型的RB防御需要非西比尔(良好)ID来消耗至少与对手一样多的资源。我们提出了一种新的防御,埃尔戈,它保证(1)总有一个坏的少数;以及(2)在显著攻击期间,好的ID消耗的资源渐近地少于坏的ID。具体地说,尽管流失率很高,但好的ID RB率是O(TJ+J),其中T是对手的RB率,J是好的ID加入率。我们证明了这个RB速率对于一大类算法是渐近最优的,并且我们实证地证明了Ergo的好处。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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