Evaluating Gradient Inversion Attacks and Defenses in Federated Learning (2112.00059v1)

Abstract: Gradient inversion attack (or input recovery from gradient) is an emerging threat to the security and privacy preservation of Federated learning, whereby malicious eavesdroppers or participants in the protocol can recover (partially) the clients' private data. This paper evaluates existing attacks and defenses. We find that some attacks make strong assumptions about the setup. Relaxing such assumptions can substantially weaken these attacks. We then evaluate the benefits of three proposed defense mechanisms against gradient inversion attacks. We show the trade-offs of privacy leakage and data utility of these defense methods, and find that combining them in an appropriate manner makes the attack less effective, even under the original strong assumptions. We also estimate the computation cost of end-to-end recovery of a single image under each evaluated defense. Our findings suggest that the state-of-the-art attacks can currently be defended against with minor data utility loss, as summarized in a list of potential strategies. Our code is available at: https://github.com/Princeton-SysML/GradAttack.

• The paper demonstrates that gradient inversion attacks can recover sensitive data by exploiting assumptions like known BatchNorm statistics and access to private labels.
• It reveals that relaxing these assumptions significantly degrades attack efficacy, highlighting a critical trade-off in defense strategies.
• The study evaluates defenses such as gradient perturbation and input encoding, offering actionable insights for strengthening privacy in federated learning.

Evaluating Gradient Inversion Attacks and Defenses in Federated Learning

The scholarly article "Evaluating Gradient Inversion Attacks and Defenses in Federated Learning" systematically addresses gradient inversion attacks within the context of federated learning, elucidating both vulnerabilities and protective mechanisms. This document emerges as a critical evaluation of the nascent threat posed by gradient inversion attacks which aim to reconstruct clients' private data from gradient information shared during federated training.

Overview of Gradient Inversion Attacks

Federated learning is a popular paradigm for training models across decentralized devices, where data remains local and only model updates or gradients are shared. Although this setup is privacy-conscious, recent studies revealed that these gradients can be inverted to recover sensitive training data—a process referred to as gradient inversion. This paper explores several gradient inversion techniques, notably the attacks proposed by previous researchers that hinge on recovering input data by treating gradient information as an optimization problem. The state-of-the-art methods involve significant assumptions, such as knowledge of batch normalization statistics and private labels, which boosts the efficacy of the attack.

Assumptions Behind Attacks

The paper critically assesses the assumptions plugged into these attacks. By touting non-trivial premises such as "BatchNorm statistics are known" or "the attacker has access to private labels," current leading attack methodologies claim proficiency in recovering high-resolution data, albeit under somewhat contrived conditions. The analysis suggests that relaxing these assumptions can significantly degrade attack efficacy. For instance, when attackers lack access to batch normalization statistics, the fidelity of reconstructed data deteriorates markedly for both low- and high-resolution images.

Defense Mechanisms Evaluated

The presented paper rigorously evaluates various defense strategies against gradient inversion. These defenses can broadly be classified into three categories:

1. Gradient Perturbation: Involves techniques like gradient pruning—removing small magnitude components—to obfuscate sensitive information. While early studies suggested significant pruning levels might protect against attacks, this research indicates that state-of-the-art attacks necessitate much higher pruning ratios, leading to substantial computation overhead and accuracy loss.
2. Input Encryption via Weak Encoding: Strategies like MixUp and InstaHide manipulate input data before training, creating encodings that complicate direct data recovery. The paper finds that while basic MixUp is inadequate, variations like InstaHide significantly hinder attack performance, especially when combined with gradient pruning.
3. Secure Protocols for Gradient Sharing: While cryptographic approaches such as homomorphic encryption are mentioned, the paper focuses less on these due to their high computational burden and setup complexity.

Practical Implications and Speculations

Practically, this work provides recommendations for securing federated learning systems. Using large batch sizes appears effective in diminishing attack success rates. The combination of gradient pruning with encoding schemes like InstaHide offers a promising trade-off between maintaining model accuracy and preventing data leakage. The findings elucidate the need for federated learning architectures to adopt layered defenses, moving beyond singular countermeasures.

For theoretic and research implications, this exploration directs future inquiry towards refining defenses that maintain utility while deterring advanced attacks. Speculations for AI development include increased emphasis on ensuring security alongside performance, as federated learning grows increasingly prominent across sensitive domains.

In conclusion, the paper delivers an exhaustive analysis of how gradient inversion attacks challenge federated learning and the efficacy of current defenses. It incites crucial discussions on privacy-preserving mechanisms and drives innovation within the federated learning community. The intricate understanding of attack assumptions coupled with empirical evaluations of defenses will guide future enhancements ensuring federated learning remains a robust privacy-preserving framework.