Privacy Token: Surprised to Find Out What You Accidentally Revealed

2025-02-05

AI Summary

To address the challenge of real-time, attack-free assessment of privacy risks arising from gradient leakage during deep learning training, this paper proposes the Privacy Token mechanism. It dynamically extracts and encodes private gradient features—jointly with data features—and quantifies the association strength between gradients and original data via mutual information, a continuous, differentiable metric. Unlike conventional approaches, it requires no posterior attack simulation, enabling proactive, fine-grained privacy monitoring throughout training. Experiments across multiple benchmark models and datasets demonstrate that Privacy Tokens achieve high sensitivity in detecting sensitive information leakage, significantly outperforming traditional attack-based posterior evaluation methods. The framework is lightweight, interpretable, and embeddable, providing real-time, principled privacy assurance for secure model deployment in privacy-sensitive applications.

Technology Category

• Machine Learning: Privacy
• Natural Language Processing: Interpretability, Analysis, and Evaluation of NLP Models
• Computer Vision: Bias, Fairness & Privacy

Application Category

• Security and Privacy: Large-scale security measurements
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

The widespread deployment of deep learning models in privacy-sensitive domains has amplified concerns regarding privacy risks, particularly those stemming from gradient leakage during training. Current privacy assessments primarily rely on post-training attack simulations. However, these methods are inherently reactive, unable to encompass all potential attack scenarios, and often based on idealized adversarial assumptions. These limitations underscore the need for proactive approaches to privacy risk assessment during the training process. To address this gap, we propose the concept of privacy tokens, which are derived directly from private gradients during training. Privacy tokens encapsulate gradient features and, when combined with data features, offer valuable insights into the extent of private information leakage from training data, enabling real-time measurement of privacy risks without relying on adversarial attack simulations. Additionally, we employ Mutual Information (MI) as a robust metric to quantify the relationship between training data and gradients, providing precise and continuous assessments of privacy leakage throughout the training process. Extensive experiments validate our framework, demonstrating the effectiveness of privacy tokens and MI in identifying and quantifying privacy risks. This proactive approach marks a significant advancement in privacy monitoring, promoting the safer deployment of deep learning models in sensitive applications.

Problem

• Proactive privacy risk assessment
• Real-time private information leakage
• Quantifying privacy leakage with Mutual Information

Innovation

• Privacy tokens: real-time risk measurement
• Mutual Information: quantifies privacy leakage
• Proactive approach: enhances privacy monitoring