通过信息密度量化隐私
Quantifying Privacy via Information Density
AI总结：
本文通过信息密度上下界约束，建立了局部信息隐私、非对称局部信息隐私、逐点最大泄漏与局部差分隐私之间的新关系，并证明信息密度下界与风险规避型对手在猜测框架和代价函数框架下的等价性，为隐私机制设计提供理论依据。
摘要：
我们研究了利用信息密度来度量私有随机变量与公开随机变量之间信息泄漏的隐私度量之间的关系。首先，我们证明，从上方或下方约束信息密度，分别意味着对信息密度施加下界或上界。利用这一结果，我们在局部信息隐私、非对称局部信息隐私、逐点最大泄漏和局部差分隐私之间建立了新的关系。我们进一步提供了这些关系在隐私机制设计中的应用。此外，我们给出了表明信息密度下界与风险规避型对手之间等价性的陈述。更具体地说，我们证明了猜测框架与代价函数框架之间的等价性，该等价性可导出所期望的信息密度下界。
英文摘要：
We examine the relationship between privacy metrics that utilize information density to measure information leakage between a private and a disclosed random variable. Firstly, we prove that bounding the information density from above or below in turn implies a lower or upper bound on the information density, respectively. Using this result, we establish new relationships between local information privacy, asymmetric local information privacy, pointwise maximal leakage and local differential privacy. We further provide applications of these relations to privacy mechanism design. Furthermore, we provide statements showing the equivalence between a lower bound on information density and risk-averse adversaries. More specifically, we prove an equivalence between a guessing framework and a cost-function framework that result in the desired lower bound on the information density.
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。
