Partial sensitivity analysis in differential privacy

TL;DR

This paper introduces partial sensitivity analysis in differential privacy, enabling feature-level privacy impact assessment using automatic differentiation, with applications to private data queries and neural network training.

Contribution

It extends individual RDP by defining partial sensitivity, allowing feature-wise privacy influence measurement through symbolic automatic differentiation.

Findings

Feature-level contribution of private attributes to DP guarantees.

Partial sensitivity analysis applied to neural network input pixels.

Enhanced understanding of input feature influence on privacy loss.

Abstract

Differential privacy (DP) allows the quantification of privacy loss when the data of individuals is subjected to algorithmic processing such as machine learning, as well as the provision of objective privacy guarantees. However, while techniques such as individual R\'enyi DP (RDP) allow for granular, per-person privacy accounting, few works have investigated the impact of each input feature on the individual's privacy loss. Here we extend the view of individual RDP by introducing a new concept we call partial sensitivity, which leverages symbolic automatic differentiation to determine the influence of each input feature on the gradient norm of a function. We experimentally evaluate our approach on queries over private databases, where we obtain a feature-level contribution of private attributes to the DP guarantee of individuals. Furthermore, we explore our findings in the context of neural network training on synthetic data by investigating the partial sensitivity of input pixels on an image classification task.