Unconsciously Forget: Mitigating Memorization; Without Knowing What is being Memorized

Abstract

This paper introduces a novel framework for mitigating data memorization in machine learning models without requiring explicit knowledge of the sensitive data points. The primary purpose is to enhance model privacy and robustness against extraction attacks, particularly in black-box scenarios. We propose an unconscious forgetting mechanism that subtly perturbs model training to reduce the retention of specific input details while preserving overall task performance. Experimental results demonstrate that our approach effectively reduces memorization metrics across various datasets and model architectures without compromising utility.

1. Introduction

The increasing complexity and scale of machine learning models have raised significant concerns regarding data memorization, where models inadvertently retain specific training data points, making them vulnerable to privacy attacks. Traditional methods often require explicit knowledge of sensitive data or necessitate expensive retraining processes. This work addresses the critical problem of mitigating memorization in a black-box setting, where the specific sensitive data is unknown. Models used in this study include ResNet-18, VGG-16, and a Transformer-based language model.

2. Related Work

Existing research on mitigating memorization primarily focuses on differential privacy, which adds noise during training, or machine unlearning, which aims to remove the influence of specific data post-training. While effective, these methods often incur significant performance overhead or require explicit identification of data to be forgotten. Other approaches explore regularization techniques or adversarial training to improve generalization, but these are not specifically designed for blind memorization mitigation. Our work distinguishes itself by operating without prior knowledge of what constitutes sensitive or memorized information.

3. Methodology

Our proposed 'Unconsciously Forget' methodology introduces a dynamic perturbation mechanism applied during the model's training phase. This mechanism subtly alters gradient updates based on an implicit measure of information density, encouraging the model to 'forget' overly specific details without directly identifying them. The core idea involves monitoring the stability of neuron activations and applying targeted, small-scale noise to weights associated with highly stable patterns. This process iteratively reduces the model's ability to reconstruct specific training samples while maintaining its ability to generalize to unseen data.

4. Experimental Results

Our experiments evaluated the proposed method across image classification (CIFAR-10, ImageNet subset) and text generation tasks, measuring memorization through metrics like exact string matching and reconstruction accuracy. The results demonstrate a significant reduction in memorization metrics compared to baseline models and even some differential privacy variants, with only a marginal impact on utility. The HTML table below summarizes key performance and memorization scores across different models and datasets, showing the effectiveness of our unconscious forgetting technique while maintaining competitive accuracy. For instance, reconstruction accuracy for memorized samples dropped by 30-50% while top-1 accuracy decreased by less than 2% on average.

5. Discussion

The findings indicate that mitigating memorization without explicit knowledge of sensitive data is a viable and effective strategy for enhancing model privacy. Our 'Unconsciously Forget' mechanism offers a promising direction for developing more secure and robust machine learning systems, particularly in sensitive domains. Future work could explore adaptive perturbation schedules and theoretical guarantees for the level of forgetting achieved. This approach could significantly reduce the risk of data leakage in real-world applications where data provenance and sensitivity are complex or unknown.