论文标题
贝叶斯安全：不是那么普通的度量
Bayes Security: A Not So Average Metric
论文作者
论文摘要
安全系统设计师赞成最差的安全指标，例如由于强大的保证提供了差异隐私（DP）的指标。不利的一面是，这些保证导致对系统性能的惩罚。在本文中，我们研究了贝叶斯安全，这是一个受密码优势启发的安全指标。与DP类似，贝叶斯安全i）独立于对手的先验知识，ii）它捕获了两个最脆弱的秘密（例如，数据记录）的最坏情况； iii）很容易组成，促进安全分析。此外，贝叶斯安全性IV）可以始终以黑盒方式估算，与DP相反，当形式分析不可行时，这很有用； v）在高安全性制度中提供了更好的公用事业安全权衡，因为它量化了特定威胁模型的风险，而不是威胁性不足的指标，例如DP。我们制定了围绕贝叶斯安全性的理论，并就众所周知的指标进行了详尽的比较，并确定了贝叶斯安全对设计师有利的方案。
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.
