利用DPAR改善用户密码：一个数据驱动的密码推荐系统
Improving Users' Passwords with DPAR: a Data-driven Password Recommendation System
AI总结：
本文提出基于9.05亿泄露密码的数据驱动密码推荐系统DPAR，通过分析用户原密码给出保持易记性的强化建议，实验证明其平均提升密码强度34.8位且36.6%用户直接采纳。
摘要：
密码是在线的主要身份验证方法，但即使有密码策略和强度计，用户仍然难以创建既强又易记的密码。在本文中，我们提出了DPAR：一个基于9.05亿个泄露密码数据集的数据驱动密码推荐系统。DPAR通过分析用户给定的密码并建议具体的调整来生成密码推荐，这些调整会使其更强，同时保持其易记性并与原始密码相似。我们进行了两项研究来评估我们的方法：验证生成密码的可记忆性（n=317），以及评估DPAR推荐相对于密码强度计的强度和回忆能力（n=441）。在一项随机实验中，我们表明DPAR平均将密码强度提高了34.8位，并且没有显著影响用户回忆密码的能力。此外，36.6%的用户逐字接受了DPAR的推荐。我们讨论了我们的发现及其对通过推荐系统增强密码管理的意义。
英文摘要：
Passwords are the primary authentication method online, but even with password policies and meters, users still find it hard to create strong and memorable passwords. In this paper, we propose DPAR: a Data-driven PAssword Recommendation system based on a dataset of 905 million leaked passwords. DPAR generates password recommendations by analyzing the user's given password and suggesting specific tweaks that would make it stronger while still keeping it memorable and similar to the original password. We conducted two studies to evaluate our approach: verifying the memorability of generated passwords (n=317), and evaluating the strength and recall of DPAR recommendations against password meters (n=441). In a randomized experiment, we show that DPAR increased password strength by 34.8 bits on average and did not significantly affect the ability to recall their password. Furthermore, 36.6% of users accepted DPAR's recommendations verbatim. We discuss our findings and their implications for enhancing password management with recommendation systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
