Reconstruction and Membership Inference Attacks against Generative Models (1906.03006v1)

Abstract: We present two information leakage attacks that outperform previous work on membership inference against generative models. The first attack allows membership inference without assumptions on the type of the generative model. Contrary to previous evaluation metrics for generative models, like Kernel Density Estimation, it only considers samples of the model which are close to training data records. The second attack specifically targets Variational Autoencoders, achieving high membership inference accuracy. Furthermore, previous work mostly considers membership inference adversaries who perform single record membership inference. We argue for considering regulatory actors who perform set membership inference to identify the use of specific datasets for training. The attacks are evaluated on two generative model architectures, Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), trained on standard image datasets. Our results show that the two attacks yield success rates superior to previous work on most data sets while at the same time having only very mild assumptions. We envision the two attacks in combination with the membership inference attack type formalization as especially useful. For example, to enforce data privacy standards and automatically assessing model quality in machine learning as a service setups. In practice, our work motivates the use of GANs since they prove less vulnerable against information leakage attacks while producing detailed samples.

Analyzing Membership Inference Attacks on Generative Models

The paper "Monte Carlo and Reconstruction Membership Inference Attacks against Generative Models" presents a detailed investigation into privacy vulnerabilities specifically associated with generative models. It tackles the problem of membership inference, a type of threat where adversaries aim to determine if a specific data point was part of the training dataset. This paper contributes by evaluating novel methodologies to address this challenge, particularly within the context of Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs), which are popular generative models.

The authors thoroughly discuss two types of attackers: one interested in a particular record (Single MI) and another that targets a set of records (Set MI). These attack frameworks are significant in both academic and regulatory discussions, as they delve into potential misuse of sensitive data by these advanced models.

Innovative Approaches

1. Monte Carlo-based Attack: This technique involves using Monte Carlo integration to estimate the likelihood of generated samples being very close to potential training records. By focusing only on the closest samples, this method differentiates itself from the Euclidean distance-based methods and is adaptable to various generative models. It has demonstrated considerable performance improvements, with accuracy rates approaching 100% under certain conditions.
2. Reconstruction Attack: Specifically tailored for VAEs, this attack exploits the fact that VAEs tend to reconstruct training data more accurately. The proficiency of this attack lies in its ability to achieve up to 100% accuracy for both single and set membership inference within certain datasets.

Implications and Observations

The paper's findings align with those in the broader field that indicate overfitting as a primary factor enhancing the success of membership inference attacks. This relationship between overfitting and membership inference was evidenced in VAEs and GANs, with VAEs proving more susceptible across different datasets. The attacks maintained high accuracy despite reducing training data or increasing dropout rates, a typical regularization technique.

The paper further underscores the practical importance of these attacks for regulatory bodies aiming to ensure data privacy compliance. Notably, the Set MI attack plays a crucial role where regulators suspect data misuse but lack direct access to training data.

Future Directions

While the research exhibits potential mitigative approaches such as increasing training data or employing dropout, these methods concurrently degrade the quality of generated samples. Therefore, future advancements could focus on balancing model fidelity with privacy guarantees.

The paper sets a foundation for robust evaluation frameworks for membership inference in generative models. Continuous exploration in this area could provide insights into developing more sophisticated mechanisms for protecting against such privacy attacks while sustaining the functional integrity of these models.

In summary, this research effectively demonstrates the applicability and effectiveness of novel membership inference attacks on generative models, enriching our understanding of privacy risks in machine learning frameworks. It opens avenues for further exploration regarding secure model design, particularly in industries where data sensitivity is paramount.