Privacy Risks of Tabular Generative Adversarial Networks: Re-identification Attacks and Reconstruction Attacks

Core Concepts

Generative models, particularly tabular GANs, can pose major privacy risks by leaking sensitive information from the training data through memorization. Attackers can exploit this vulnerability to recover private training samples using re-identification attacks and reconstruction attacks.

Abstract

The paper investigates the privacy risks associated with the use of generative adversarial networks (GANs) for creating synthetic tabular datasets. It focuses on re-identification attacks, where attackers aim to select synthetic samples that are likely to correspond to memorized training samples based on their proximity to the nearest synthetic records. The paper considers multiple attack scenarios where attackers have different levels of access to the generative model and predictive models. It analyzes the effectiveness of selection attacks based on the vicinity of synthetic samples, as well as reconstruction attacks using evolutionary multi-objective optimization to perturb synthetic samples closer to the training space. The results indicate that attackers can pose major privacy risks by selecting synthetic samples that are likely representative of memorized training samples. The privacy threats increase when the attacker has knowledge of or black-box access to the generative models. Reconstruction attacks through multi-objective optimization further increase the risk of identifying confidential samples. The paper also compares the performance of the proposed attacks against benchmark membership inference attacks, demonstrating the effectiveness of the re-identification and reconstruction approaches.

Stats

Generative models can potentially leak sensitive information from the training data due to overfitting and memorization. Tabular data often encapsulates sensitive information about individuals or records, making it crucial to understand the privacy risks associated with generative models. Existing work on privacy attacks has primarily focused on discriminative models, while the implications of potential privacy risks on tabular data generated by GANs remain understudied.

Quotes

"Concerningly though, private tabular data often encapsulates sensitive information about individuals or records. To train models that overfit on the data induces then a privacy risk, since such overfitting may be due to some form of memorising data samples by the models." "We therefore hypothesise that the increased accessibility of tabular GAN models can threaten the privacy of sensitive information. Moreover, from intuitive perspectives the risk seems heightened for smaller and lower-dimensional datasets, and for mixed-type datasets where categorical features can take a finite range of values."

Key Insights Distilled From

Privacy Re-identification Attacks on Tabular GANs

by Abdallah Als... at arxiv.org 04-02-2024

https://arxiv.org/pdf/2404.00696.pdf

Deeper Inquiries

How can the proposed re-identification and reconstruction attacks be extended to other types of generative models beyond tabular GANs

The proposed re-identification and reconstruction attacks can be extended to other types of generative models beyond tabular GANs by adapting the methodology to suit the characteristics of the specific model. For instance, in the case of image-based GANs, the attack strategy could involve perturbing pixel values or image features to bring the synthetic samples closer to the training data distribution. The concept of selecting samples based on proximity and prediction error can still be applied, but the implementation would need to consider the unique attributes of image data. Additionally, for text-based generative models, the attacks could involve manipulating word embeddings or text representations to optimize for similarity to the training data. The use of evolutionary multi-objective optimization could be tailored to adjust text features or sequences to align with the original training samples. Overall, the key is to understand the data representation and generation process of the specific generative model in order to adapt the re-identification and reconstruction attacks effectively.

What are the potential limitations and drawbacks of using evolutionary multi-objective optimization for reconstruction attacks, and how can they be addressed

One potential limitation of using evolutionary multi-objective optimization for reconstruction attacks is the computational complexity and time required to find optimal solutions, especially for large datasets with high dimensionality. The optimization process may be resource-intensive and may not scale well to real-world applications where efficiency is crucial. To address this limitation, techniques such as parallelization of the optimization process, optimization algorithm enhancements, and feature reduction methods could be employed to streamline the process and improve computational efficiency. Additionally, exploring alternative optimization algorithms that are more suited to the specific characteristics of the data and objectives could help mitigate the drawbacks of using evolutionary multi-objective optimization. Another drawback could be the sensitivity of the optimization process to the choice of objective weights and parameters. Fine-tuning these parameters may require domain expertise and could introduce bias or suboptimal solutions. Robust sensitivity analysis and parameter tuning strategies could help address this issue.

What are the broader implications of the privacy risks identified in this work, and how can they inform the development of more privacy-preserving generative modeling techniques

The privacy risks identified in this work have broader implications for the development and deployment of generative modeling techniques across various domains. These risks highlight the potential for sensitive information leakage and unauthorized access to private data through synthetic datasets generated by generative models. To address these implications and inform the development of more privacy-preserving generative modeling techniques, several strategies can be considered. These include: Incorporating differential privacy mechanisms into the generative modeling process to ensure that the generated synthetic data does not reveal sensitive information about the training data. Implementing robust privacy-preserving techniques such as federated learning, homomorphic encryption, and secure multi-party computation to protect the privacy of the training data during the generative modeling process. Conducting thorough privacy impact assessments and risk analyses before deploying generative models to identify and mitigate potential privacy risks proactively. Enhancing transparency and accountability in the data generation process by documenting and disclosing the methods used to generate synthetic data and the potential privacy implications to stakeholders. By addressing these broader implications and integrating privacy-preserving measures into generative modeling practices, researchers and practitioners can develop more secure and privacy-aware generative models that safeguard sensitive information and uphold data privacy standards.