Application-driven Privacy-preserving Data Publishing with Correlated Attributes

Abstract

Recent advances in computing have allowed for the possibility to collect large amounts of data on personal activities and private living spaces. To address the privacy concerns of users in this environment, we propose a novel framework called PR-GAN that offers privacy-preserving mechanism using generative adversarial networks. Given a target application, PR-GAN automatically modifies the data to hide sensitive attributes -- which may be hidden and can be inferred by machine learning algorithms -- while preserving the data utility in the target application. Unlike prior works, the public's possible knowledge of the correlation between the target application and sensitive attributes is built into our modeling. We formulate our problem as an optimization problem, show that an optimal solution exists and use generative adversarial networks (GAN) to create perturbations. We further show that our method provides privacy guarantees under the Pufferfish framework, an elegant generalization of the differential privacy that allows for the modeling of prior knowledge on data and correlations. Through experiments, we show that our method outperforms conventional methods in effectively hiding the sensitive attributes while guaranteeing high performance in the target application, for both property inference and training purposes. Finally, we demonstrate through further experiments that once our model learns a privacy-preserving task, such as hiding subjects' identity, on a group of individuals, it can perform the same task on a separate group with minimal performance drops.

arXiv comment12 pages

References in corpus (8)

• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Deep Learning with Differential Privacy
• Federated Learning: Strategies for Improving Communication Efficiency
• BEGAN: Boundary Equilibrium Generative Adversarial Networks
• Data Decisions and Theoretical Implications when Adversarially Learning Fair Representations
• Generating Adversarial Examples with Adversarial Networks
• Censoring Representations with an Adversary
• Minimax Filter: Learning to Preserve Privacy from Inference Attacks

Cited by in corpus (5)

• Generative Adversarial Networks: A Survey Towards Private and Secure Applications
• PriMask: Cascadable and Collusion-Resilient Data Masking for Mobile Cloud Inference
• Uncertainty-Autoencoder-Based Privacy and Utility Preserving Data Type Conscious Transformation
• Distributed generation of privacy preserving data with user customization
• Generating private data with user customization