Partial sensitivity analysis in differential privacy

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 Rényi 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.

References in corpus (7)

• Deep Learning with Differential Privacy
• Renyi Differential Privacy
• Discrete Distribution Estimation under Local Privacy
• Differential Privacy Has Disparate Impact on Model Accuracy
• Individual Privacy Accounting via a Renyi Filter
• Decision Making with Differential Privacy under a Fairness Lens
• Sensitivity analysis in differentially private machine learning using hybrid automatic differentiation