用径向曲线表示3D人脸，实现更优的隐私保护与形状保持。
Gaussian Differentially Private Human Faces Under a Face Radial Curve Representation
- 提出人脸径向曲线表示法，将3D人脸转为函数集合。
- 在相同隐私预算下，噪声更低且平均脸形状保留更好。
- 方法适用于函数型数据与盘状曲面，不局限于人脸。
本文研究如何在高斯差分隐私（GDP）框架下安全发布3D人脸数据。人脸结构复杂且与身份紧密关联，保护此类数据面临维度高、隐私风险大的挑战。我们拓展近似差分隐私技术至GDP框架，提出一种新的面部径向曲线表示法，将3D人脸建模为一组函数，并结合所提出的GDP函数型数据机制进行隐私保护。通过引入形状分析工具，在注入噪声的同时有效保持人脸整体形态。实验表明，该方法在相同隐私预算下比传统方法注入更少噪声，且能较好保留平均人脸形状。该机制包含两个通用组件：一是适用于函数值汇总的通用模块，二是适用于盘状曲面的通用模块，因此不仅限于人脸应用。
原文摘要 · Abstract (English)
In this paper we consider the problem of releasing a Gaussian Differentially Private (GDP) 3D human face. The human face is a complex structure with many features and inherently tied to one's identity. Protecting this data, in a formally private way, is important yet challenging given the dimensionality of the problem. We extend approximate DP techniques for functional data to the GDP framework. We further propose a novel representation, face radial curves, of a 3D face as a set of functions and then utilize our proposed GDP functional data mechanism. To preserve the shape of the face while injecting noise we rely on tools from shape analysis for our novel representation of the face. We show that our method preserves the shape of the average face and injects less noise than traditional methods for the same privacy budget. Our mechanism consists of two primary components, the first is generally applicable to function value summaries (as are commonly found in nonparametric statistics or functional data analysis) while the second is general to disk-like surfaces and hence more applicable than just to human faces.
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