贝叶斯安全：一个并非平均的度量指标
Bayes Security: A Not So Average Metric
AI总结：
本文提出贝叶斯安全度量，借鉴密码学优势思想，在保持最坏情况保证和可组合性的同时，支持黑盒估计并改善高安全场景下的效用-安全权衡，并通过与现有度量比较明确其适用场景。
摘要：
安全系统设计者倾向于采用最坏情况安全度量，例如由差分隐私（DP）导出的度量，因为它们能提供强有力的保证。但其代价是这些保证会对系统性能造成较高的惩罚。本文研究了贝叶斯安全，这是一种受密码学优势启发的安全度量。与差分隐私类似，贝叶斯安全 i) 独立于对手的先验知识；ii) 刻画两个最脆弱秘密（如数据记录）的最坏情况；iii) 易于组合，便于进行安全分析。此外，贝叶斯安全 iv) 能够以黑盒方式一致地进行估计，这与差分隐私不同，在无法进行形式化分析时非常有用；并且 v) 在高安全机制下提供了更好的效用-安全权衡，因为它量化的是特定威胁模型下的风险，而非像差分隐私那样与威胁无关的度量。我们围绕贝叶斯安全建立了一套理论，并针对众所周知的度量进行了全面比较，识别出贝叶斯安全对设计者更具优势的场景。
英文摘要：
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.
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。
