Privacy in machine learning models has become a critical concern due to membership inference attacks (MIA). These attacks measure whether specific data points were part of a model's training data. Understanding MIA is critical as it assesses inadvertent exposure of information when models are trained on diverse data sets. The scope of MIA covers various scenarios, from statistical models to federated and privacy-preserving machine learning. Initially based on summary statistics, MIA methods have evolved, using various hypothesis testing strategies and approaches, especially in deep learning algorithms.
Previous MIA approaches have faced significant challenges. Despite improvements in attack effectiveness, computational demands have made many privacy audits impractical. Some state-of-the-art methods, particularly for generalized models, border on random guessing when limited by computational resources. Furthermore, the lack of clear and interpretable means to compare different attacks has led to their mutual dominance, where each attack outperforms the other based on different scenarios. This complexity requires the development of more robust yet efficient attacks to effectively assess privacy risks. The computational expense associated with existing attacks has limited their practicality, underscoring the need for novel strategies that achieve high performance within limited computational budgets.
In this context, a new paper was published to propose a novel attack approach within the scope of membership inference attacks (MIA). Membership inference attacks, which aim to discern whether a specific data point was used during the training of a given machine learning model θ, are described as an indistinguishability game between a challenger (algorithm) and an adversary (auditor). privacy). This involves scenarios where a model θ is trained with or without data point x. The adversary's task is to infer, based on x, the trained model θ and its knowledge of the data distribution, which scenario it is in within these two worlds.
The new Membership Inference Attack (MIA) methodology introduces a precise approach to construct two distinct worlds where x is a member or non-member of the training set. Unlike previous methods that simplified these constructs, this new attack meticulously composes the null hypothesis by replacing x with random data points from the population. This design leads to many pairwise likelihood ratio tests to measure the membership of x relative to other data points z. The attack aims to gather substantial evidence favoring the presence of x in the training set over a random z, offering a more nuanced leakage analysis. This novel method calculates the likelihood ratio corresponding to x and z, distinguishing between scenarios in which x is a member and a non-member using a likelihood ratio test.
This methodology, called relative membership inference attack (RMIA), leverages population data and reference models to improve the power and robustness of the attack against variations in adversary prior knowledge. It introduces a refined likelihood ratio test that effectively measures the distinguishability between x and any z based on changes in their probabilities when conditioned on θ. Unlike existing attacks, this method ensures a more calibrated approach, avoiding dependencies on an uncalibrated magnitude or bypassing essential calibration with population data. Through meticulous pairwise likelihood ratio calculation and Bayesian approach, RMIA emerges as a robust, high-powered, and cost-effective attack, outperforming previous state-of-the-art methods in several scenarios.
The authors compared RMIA with other membership inference attacks using datasets such as CIFAR-10, CIFAR-100, CINIC-10, and Purchase-100. RMIA consistently outperformed other attacks, especially with a limited number of reference models or in offline scenarios. Even with few models, RMIA showed results close to scenarios with more models. With abundant reference models, RMIA maintained a slight advantage in AUC and noticeably higher TPR at zero FPR compared to LiRA. Its performance improved with more queries, demonstrating its effectiveness in various scenarios and data sets.
To conclude, the paper presents RMIA, a relative membership inference attack method, which demonstrates its superiority over existing attacks in membership identification within machine learning models. RMIA excels in scenarios with limited reference models and shows strong performance on various data sets and model architectures. Furthermore, this efficiency makes RMIA a practical and viable option for privacy risk analysis, especially in scenarios where resource limitations are a concern. Its flexibility, scalability, and balance between accuracy and false positives position RMIA as a reliable and adaptable method for membership inference attacks, offering promising applications in privacy risk analysis tasks for machine learning models.
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Mahmoud is a PhD researcher in machine learning. He also owns a
Bachelor's degree in Physical Sciences and Master's degree in
telecommunications systems and networks. Your current areas of
The research concerns computer vision, stock market prediction and depth.
learning. He produced several scientific articles on the relationship of people.
identification and study of the robustness and stability of depths
networks.
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