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Picking vs. Guessing Secrets: A Game-Theoretic Analysis

机译:采摘与猜测秘密:游戏 - 理论分析

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Choosing a hard-to-guess secret is a prerequisite in many security applications. Whether it is a password for user authentication or a secret key for a cryptographic primitive, picking it requires the user to trade-off usability costs with resistance against an adversary: a simple password is easier to remember but is also easier to guess, likewise, a shorter cryptographic key may require fewer computational and storage resources but it is also easier to attack. A fundamental question is how one can optimally resolve this trade-off. A big challenge is the fact that an adversary can also utilize the knowledge of such usability vs. security trade-offs to strengthen its attack. In this paper, we propose a game-theoretic framework for analyzing the optimal trade-offs in the face of strategic adversaries. We consider two types of adversaries: those limited in their number of tries, and those that are ruled by the cost of making individual guesses. For each type, we derive the mutually-optimal decisions as Nash Equilibria, the strategically pessimistic decisions as maximin, and optimal commitments as Strong Stackelberg Equilibria of the game. We establish that when the adversaries are faced with a capped number of guesses, the user's optimal trade-off is a uniform randomization over a subset of the secret domain. On the other hand, when the attacker strategy is ruled by the cost of making individual guesses, Nash Equilibria may completely fail to provide the user with any level of security, signifying the crucial role of credible commitment for such cases. We illustrate our results using numerical examples based on real-world samples and discuss some policy implications of our work.
机译:选择难以猜测的秘密是许多安全应用程序中的先决条件。无论是用户身份验证的密码还是加密原语的密钥,选择它要求用户对抗对手的抵制权衡可用性成本:一个简单的密码更容易记住,但同样也更容易猜测,更短的加密密钥可能需要更少的计算和存储资源,但它也更容易攻击。基本问题是如何最佳地解决此权衡。一个重要的挑战是,对手也可以利用这种可用性与安全权衡的知识来加强攻击。在本文中,我们提出了一种游戏理论框架,用于分析面对战略对手的最佳权衡。我们考虑两种类型的对手:他们尝试的人数有限,那些被制作个人猜测的成本裁定的对手。对于每种类型,我们将相互最佳的决策导致纳什均衡,战略性悲观决策作为最大值,以及最佳承诺,作为比赛的强大架空均衡。我们确定当对手面临着封闭的猜测数量时,用户的最佳折衷是在秘密域的子集上均匀随机化。另一方面,当攻击者战略被制造个人猜测的成本统治时,纳什均衡可能完全无法向用户提供任何级别的安全性,从而表示可信承诺对此类案件的关键作用。我们使用基于现实世界样本的数值示例说明了我们的结果,并讨论了我们工作的一些政策影响。

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