一句话：提出定量差分策略分析框架，用模型计数约束求解器量化访问控制策略的相对宽松程度。
云服务中访问控制策略复杂且易出错，可能导致意外未授权访问，因此需要能比较和量化策略宽松程度的技术。本文提出一个定量且差分的策略分析框架，不仅判断一条策略是否比另一条更宽松，还量化访问控制策略的相对宽松程度。作者利用模型计数约束求解器计算宽松性，并提出一种启发式方法，对从访问控制策略中抽取的约束进行变换，显著提升模型计数性能。该框架应用于 Amazon AWS Identity and Access Management（IAM）策略语言和 Microsoft Azure 策略语言，展示了方法的有效性。
Due to ubiquitous use of software services, protecting the confidentiality of private information stored in compute clouds is becoming an increasingly critical problem. Although access control specification languages and libraries provide mechanisms for protecting confidentiality of information, without verification and validation techniques that can assist developers in writing policies, complex policy specifications are likely to have errors that can lead to unintended and unauthorized access to data, possibly with disastrous consequences. In this paper, we present a quantitative and differential policy analysis framework that not only identifies if one policy is more permissive than another policy, but also quantifies the relative permissiveness of access control policies. We quantify permissiveness of policies using a model counting constraint solver. We present a heuristic that transforms constraints extracted from access control policies and significantly improves the model counting performance. We demonstrate the effectiveness of our approach by applying it to policies written in Amazon's AWS Identity and Access Management (IAM) policy language and Microsoft's Azure policy language.
@inproceedings{EiersSLOPB22,
title = {Quantifying permissiveness of access control policies},
author = {William Eiers and Ganesh Sankaran and Albert Li and Emily O'Mahony and Benjamin Prince and Tevfik Bultan},
booktitle = {ICSE},
year = {2022},
doi = {10.1145/3510003.3510233}
}