Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks (1911.04933v5)

Abstract: We explore the problem of selectively forgetting a particular subset of the data used for training a deep neural network. While the effects of the data to be forgotten can be hidden from the output of the network, insights may still be gleaned by probing deep into its weights. We propose a method for "scrubbing'" the weights clean of information about a particular set of training data. The method does not require retraining from scratch, nor access to the data originally used for training. Instead, the weights are modified so that any probing function of the weights is indistinguishable from the same function applied to the weights of a network trained without the data to be forgotten. This condition is a generalized and weaker form of Differential Privacy. Exploiting ideas related to the stability of stochastic gradient descent, we introduce an upper-bound on the amount of information remaining in the weights, which can be estimated efficiently even for deep neural networks.

• The paper introduces a novel scrubbing technique to remove specific data influence from network weights without requiring complete retraining.
• It leverages SGD stability and the Fisher Information Matrix to bound and suppress residual information effectively.
• Empirical results on MNIST, CIFAR-10, and Lacuna datasets show minimal KL divergence while maintaining overall model performance.

Selective Forgetting in Deep Neural Networks: A Methodological Overview

The paper "Eternal Sunshine of the Spotless Net: Selective Forgetting in Deep Networks" addresses the problem of selective forgetting within the context of deep neural networks (DNNs). The authors propose a novel methodology to specifically remove the influence of certain subsets of training data from a trained network without resorting to complete retraining. This discussion focuses on the paper's main contributions, implications for future research, and potential practical applications.

Core Contributions

The authors introduce a scrubbing technique that cleanses the weights of a neural network from information pertaining to a specified subset of the training dataset. Unlike conventional methods needing data access or retraining, this approach modifies the network's weights such that any probing of these weights becomes indistinguishable from a scenario where the forgotten data subset was never part of the training process. This concept extends beyond existing privacy methodologies like Differential Privacy, as it allows for deterministic information removal while maintaining the integrity of data not designated for forgetting.

Several key contributions include:

• Selective Forgetting Framework: The authors formalize a definition of selective forgetting, distinguishing between complete erasure of information concerning specific data and mere output obfuscation.
• Stability-Based Bound: Utilizing the stability of stochastic gradient descent, they provide an upper-bound estimate on residual information post-scrubbing, allowing for a more efficient scrubbing process that aligns closely with a retrained-from-scratch model.
• Efficient Implementation: The proposed approach leverages the Fisher Information Matrix and other approximations to suppress information without needing access to all original data, making it viable for complex networks like convolutional architectures on large datasets.

Numerical Results and Key Findings

The paper presents empirical results on datasets such as MNIST, CIFAR-10, and newly introduced Lacuna datasets, demonstrating the efficacy of the forgetting algorithm. The scrubbing procedure reduces the KL divergence between the weight distributions of original and retrained models, indicating minimal residual information about the cohort to be forgotten. Additionally, the noise added ensures that forgetting does not disproportionately degrade performance on the remaining datasets, highlighting a sophisticated trade-off between accuracy and information loss.

Practical and Theoretical Implications

Practically, this research could significantly impact areas where data privacy and regulatory compliance are critical, such as personal data deletion requests under regulations like GDPR. The proposed method's ability to apply forgetting selectively without hampering overall model utility underscores its utility in real-world applications where data retention policies are dynamic.

Theoretically, this work presents a formal bridge between the concepts of data privacy, model interpretability, and machine learning stability. It opens up new questions around the bounds of information retention and the potential for further optimization of weight perturbation strategies. The detailed exploration of forgetting in a machine learning context could incite advancements in both neural network training protocols and privacy-preserving technologies.

Future Directions

The proposed framework fosters several avenues for future research, such as:

• Exploration of Alternative Forgetting Metrics: Beyond KL divergence, other information-theoretic measures might offer nuanced insights into effective forgetting.
• Scaling and Efficiency: While the current methodology has demonstrated success on standard datasets, future work may focus on enhancing its scalability and computational efficiency for more complex models or larger datasets.
• Attack Modeling: As forgetting techniques evolve, so must the consideration of potential adversaries attempting to reconstruct forgotten information, necessitating robust evaluation scenarios.

In summary, this paper provides a comprehensive framework for selective forgetting in neural networks, harmonizing privacy needs with the practical feasibility of maintaining model accuracy. Its impact is anticipatory, as it sets a foundational approach for enduring questions about how machine learning models can responsibly manage evolving data landscapes.