Security system designers favor worst-case security metrics, such as those derived from differential privacy (DP), due to the strong guarantees they provide. On the downside, these guarantees result in a high penalty on the system's performance. In this paper, we study Bayes security, a security metric inspired by the cryptographic advantage. Similarly to DP, Bayes security i) is independent of an adversary's prior knowledge, ii) it captures the worst-case scenario for the two most vulnerable secrets (e.g., data records); and iii) it is easy to compose, facilitating security analyses. Additionally, Bayes security iv) can be consistently estimated in a black-box manner, contrary to DP, which is useful when a formal analysis is not feasible; and v) provides a better utility-security trade-off in high-security regimes because it quantifies the risk for a specific threat model as opposed to threat-agnostic metrics such as DP. We formulate a theory around Bayes security, and we provide a thorough comparison with respect to well-known metrics, identifying the scenarios where Bayes Security is advantageous for designers.

翻译：安全系统设计者倾向于最坏情况下的安全度量标准，例如从差分隐私（DP）派生出的标准，因为它们提供了强有力的保证。但缺点是，这些保证会导致系统性能受到严重影响。在本文中，我们研究了贝叶斯安全（Bayes security）——一种受密码学优势启发的安全度量标准。与DP类似，贝叶斯安全 i) 不依赖于攻击者的先验知识；ii) 针对两个最易受攻击的秘密（例如，数据记录）捕捉最坏情况；iii) 易于组合，便于安全性分析。此外，贝叶斯安全 iv) 可以在黑盒方式下进行一致估计，这与DP相反，在无法进行正式分析时尤为有用；v) 在高安全体制下提供更好的效用-安全权衡，因为它针对特定威胁模型量化风险，而非像DP那样与威胁无关的度量标准。我们围绕贝叶斯安全构建了一套理论，并与知名度量标准进行了全面比较，识别出贝叶斯安全对设计者有利的场景。