Quantifying Information Leakage in Machine Learning Models via Leave-One-Out Distinguishability

Core Concepts

We introduce an analytical framework to quantify the changes in a machine learning algorithm's output distribution following the inclusion or removal of a few data points in its training set, a notion we define as leave-one-out distinguishability (LOOD). This allows us to measure data memorization, information leakage, and the influence of training data points in machine learning.

Abstract

The paper introduces an analytical framework to quantify the changes in a machine learning model's output distribution when a few data points are added to or removed from the training set. This notion is defined as leave-one-out distinguishability (LOOD). Key highlights: LOOD can be used to measure data memorization, information leakage, and the influence of training data points on model predictions. The authors use Gaussian Processes (GPs) to model the randomness of machine learning algorithms and validate LOOD with empirical analysis of leakage using membership inference attacks. The analytical framework enables investigating the causes of leakage, such as the influence of activation functions on data memorization. The method allows identifying queries that disclose the most information about the training data in the leave-one-out setting, which can be used for accurate reconstruction of training data. The authors prove that under certain conditions, the differing data point itself is a stationary point of LOOD, suggesting it incurs maximal leakage. Experiments show that optimizing LOOD can recover the differing data point with high visual similarity, demonstrating the potential for data reconstruction attacks. The authors analyze how the choice of activation function affects the magnitude of information leakage, proving that smooth activations like GeLU induce higher leakage than non-smooth activations like ReLU.

Stats

"We observe over 140× speed up for estimating leakage and influence using our framework versus empirically measuring it over retrained models as it is performed in the literature."

Quotes

"LOOD measures privacy loss in a black-box setting, for specific datasets, and DP determines the maximum (white-box) privacy loss across all possible datasets." "The power of any MIA (true positive rate given a fixed false positive rate) is controlled by the likelihood ratio test of observing the prediction given D versus D' as the training set." "Smooth activations such that GeLU are associated with kernels that are farther away from a low rank all-constant matrix (more expressive) than kernel obtained with non-smooth activations, e.g. ReLU."

Key Insights Distilled From

Leave-one-out Distinguishability in Machine Learning

by Jiayuan Ye,A... at arxiv.org 04-18-2024

https://arxiv.org/pdf/2309.17310.pdf

Deeper Inquiries

How can the analytical LOOD framework be extended to handle more complex scenarios, such as when the differing data is a group of records or when the query is a set of data points

The analytical LOOD framework can be extended to handle more complex scenarios by considering group scenarios or multiple query points. When the differing data consists of a group of records, the LOOD framework can be adapted to calculate the statistical divergence between the output distributions of models trained on datasets that differ by a group of records. This extension would involve modifying the LOOD calculation to account for the influence of multiple records in the training set on the model's predictions. Similarly, when the query involves a set of data points, the LOOD framework can be adjusted to analyze the impact of multiple queries on the model's output distribution. By considering the collective influence of a group of records or a set of queries, the LOOD framework can provide insights into how different combinations of data points affect the model's behavior and potential information leakage.

What other machine learning concepts, beyond information leakage and influence, can be analyzed using the LOOD framework

Beyond information leakage and influence, the LOOD framework can be utilized to analyze various other machine learning concepts. One such concept is data memorization, which refers to the extent to which a model memorizes the training data. By examining the changes in the model's output distribution when specific data points are included or excluded from the training set, the LOOD framework can quantify the level of memorization and identify the data points that significantly impact the model's predictions. Additionally, the LOOD framework can be applied to study model robustness, generalization capabilities, and the impact of hyperparameters on model performance. By analyzing the changes in the output distribution under different scenarios, the LOOD framework can provide valuable insights into various aspects of machine learning models beyond information leakage and influence.

Can the insights about the effect of activation functions on information leakage be leveraged to design more privacy-preserving neural network architectures

The insights about the effect of activation functions on information leakage can be leveraged to design more privacy-preserving neural network architectures. By understanding that smooth activation functions, such as GeLU, induce higher information leakage compared to non-smooth activations like ReLU, designers can make informed choices when selecting activation functions for neural networks. To enhance privacy preservation, designers can opt for non-smooth activation functions that lead to lower information leakage. Additionally, the findings can guide the development of privacy-enhancing techniques, such as regularization methods or adversarial training, to mitigate the impact of activation functions on information leakage. By incorporating these insights into the design process, developers can create neural network architectures that prioritize privacy while maintaining performance and accuracy.