Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning (1904.01067v2)

Abstract: Machine learning (ML) has progressed rapidly during the past decade and the major factor that drives such development is the unprecedented large-scale data. As data generation is a continuous process, this leads to ML model owners updating their models frequently with newly-collected data in an online learning scenario. In consequence, if an ML model is queried with the same set of data samples at two different points in time, it will provide different results. In this paper, we investigate whether the change in the output of a black-box ML model before and after being updated can leak information of the dataset used to perform the update, namely the updating set. This constitutes a new attack surface against black-box ML models and such information leakage may compromise the intellectual property and data privacy of the ML model owner. We propose four attacks following an encoder-decoder formulation, which allows inferring diverse information of the updating set. Our new attacks are facilitated by state-of-the-art deep learning techniques. In particular, we propose a hybrid generative model (CBM-GAN) that is based on generative adversarial networks (GANs) but includes a reconstructive loss that allows reconstructing accurate samples. Our experiments show that the proposed attacks achieve strong performance.

• The paper identifies a new vulnerability called "Updates-Leak" in online learning models where the difference between model outputs before and after updates can be exploited to infer or reconstruct the training data.
• It introduces four query-based attack strategies, including single-sample methods for label inference and reconstruction, and multi-sample techniques like label distribution estimation and full dataset reconstruction using CBM-GAN.
• The research highlights the critical need for robust security measures to protect data privacy in real-time updated models and suggests future work on defense mechanisms like differential privacy.

Online Learning and Data Set Inference Vulnerabilities

The paper "Updates-Leak: Data Set Inference and Reconstruction Attacks in Online Learning" explores the potential vulnerabilities that arise when ML models, particularly those updated in an online manner, are subject to query-based information leakage attacks. This work highlights a novel attack surface wherein the differences in model outputs, pre- and post-update, can be exploited to infer the dataset used for these updates.

Online Learning and its Vulnerabilities

In contrast to traditional ML models which are trained once using a static dataset, online learning entails continuous model updates with new data streams. This leads to frequent updates in model parameters. The paper rightly identifies the risk posed by adversaries capable of probing an ML model before and after updates with identical data samples. Such adversaries could discern the characteristics of the update dataset, potentially compromising both intellectual property and data privacy.

Attack Vectors and Methodologies

Focusing on classification tasks with black-box access, the paper introduces four main attack strategies categorized into single-sample and multi-sample approaches. These strategies exploit the encoder-decoder architectural framework to leak information about the update dataset. Notably, the encoder translates changes in model outputs into a latent space representation, while the decoder attempts to reconstruct information related to the update set.

• Single-Sample Attacks: These are explored as a foundational case where each model update involves a single new data sample. The paper discusses:
  • Label Inference: This attack model can predict the label of the single update sample with high accuracy, achieving 0.96 on the CIFAR-10 dataset.
  • Reconstruction: Using an autoencoder to reconstruct the update sample, it demonstrated significant performance improvements over baseline models, highlighting the severity of privacy risks in even simplified scenarios.
• Multi-Sample Attacks: These present a more complex scenario where updates consist of multiple data samples.
  • Label Distribution Estimation: This technique uses KL-divergence minimization to approximate the distribution of labels in the update set, significantly outperforming random guessing baselines.
  • Reconstruction Using CBM-GAN: A substantial innovation is the hybrid generative model (CBM-GAN), incorporating a loss function optimized for best-matching samples, effectively reconstructing entire data sets like MNIST, even in challenging conditions.

Implications and Future Directions

The implications are multifaceted. Practically, the research emphasizes the need for stringent security measures in maintaining data privacy with models frequently updated in real-time. From a theoretical perspective, it underscores the potential for adversaries to exploit even benign-seeming operational choices in ML deployment, such as regular updates.

Looking towards future research, the development of robust defenses against such attacks is paramount. Potential directions include studying differential privacy techniques and adversarial noise quaazers to obfuscate model outputs efficiently. Furthermore, this paper calls for a deeper inspection into the variances of attack efficacy across different model architectures and datasets, suggesting broader considerations for designing future machine learning systems with built-in resilience to such vulnerabilities.

In sum, the paper provides a meticulous analysis of the vulnerabilities inherent in online learning models and sets a sturdy foundation for ensuing studies aimed at fortifying these systems against inference and reconstruction attacks.