Probabilistic password strength meters have been proved to be the most accurate tools to measure password strength. Unfortunately, by construction, they are limited to solely produce an opaque security estimation that fails to fully support the user during the password composition. In the present work, we move the first steps towards cracking the intelligibility barrier of this compelling class of meters. We show that probabilistic password meters inherently own the capability of describing the latent relation occurring between password strength and password structure. In our approach, the security contribution of each character composing a password is disentangled and used to provide explicit fine-grained feedback for the user. Furthermore, unlike existing heuristic constructions, our method is free from any human bias, and, more importantly, its feedback has a probabilistic interpretation. In our contribution: (1) we formulate interpretable probabilistic password strength meters; (2) we describe how they can be implemented via an efficient and lightweight deep learning framework suitable for client-side operability.

翻译：概率密码强度表已被证明是最精确的测量密码强度的工具。 不幸的是,通过构建,它们仅限于生成不透明的安全估计,无法在密码构成期间充分支持用户。在目前的工作中,我们迈出了第一步,以打破这种逼不得已的仪表的智能障碍。我们表明概率密码仪本身就具有描述密码强度和密码结构之间潜在关系的能力。在我们的方法中,组成密码的每个字符的安全贡献是分解的,用来为用户提供明确的精细的反馈。此外,与现有的超常结构不同,我们的方法没有人类偏见,更重要的是,其反馈有一个概率性的解释。我们的贡献:(1) 我们制定可解释的概率密码强度表;(2) 我们描述如何通过一个适合客户端可操作的高效和轻度深层次学习框架来实施这些功能。