论文标题
关于完美混淆：本地信息几何分析
On Perfect Obfuscation: Local Information Geometry Analysis
论文作者
论文摘要
我们考虑在完美混淆约束下针对特定实用性任务的隐私数据发布问题。我们建立了必要和足够的条件，以提取原始数据的特征，这些数据携带尽可能多的信息信息，同时没有透露有关敏感属性的任何信息。这个问题提出的概括了信息瓶颈和隐私渠道问题。我们采用了局部信息几何分析，该分析可为信息质量函数的球形扰动的信息耦合和轨迹构造提供有用的见解。这种分析使我们能够构建关节分布，发散传输矩阵和互信息的模态分解。通过将相互信息分解为正交模式，我们获得了有关效用属性的推断，同时满足了完美的混淆约束。此外，我们基于$χ^2 $ -DDIVERGENCE和KULLBACK-LEIBLER DIVERGENCE在Euclidean Information几何形状中开发了完美混淆的概念。
We consider the problem of privacy-preserving data release for a specific utility task under perfect obfuscation constraint. We establish the necessary and sufficient condition to extract features of the original data that carry as much information about a utility attribute as possible, while not revealing any information about the sensitive attribute. This problem formulation generalizes both the information bottleneck and privacy funnel problems. We adopt a local information geometry analysis that provides useful insight into information coupling and trajectory construction of spherical perturbation of probability mass functions. This analysis allows us to construct the modal decomposition of the joint distributions, divergence transfer matrices, and mutual information. By decomposing the mutual information into orthogonal modes, we obtain the locally sufficient statistics for inferences about the utility attribute, while satisfying perfect obfuscation constraint. Furthermore, we develop the notion of perfect obfuscation based on $χ^2$-divergence and Kullback-Leibler divergence in the Euclidean information geometry.
