A Novel Attribute Reconstruction Attack in Federated Learning

By Lingjuan Lyu, Chen Chen. 2021

47 citations recorded by the source corpus.

Federated learning (FL) emerged as a promising learning paradigm to enable a multitude of participants to construct a joint ML model without exposing their private training data. Existing FL designs have been shown to exhibit vulnerabilities which can be exploited by adversaries both within and outside of the system to compromise data privacy. However, most current works conduct attacks by leveraging gradients on a small batch of data, which is less practical in FL. In this work, we consider a more practical and interesting scenario in which participants share their epoch-averaged gradients (share gradients after at least 1 epoch of local training) rather than per-example or small batch-averaged gradients as in previous works. We perform the first systematic evaluation of attribute reconstruction attack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared epoch-averaged local model gradients can reveal sensitive attributes of local training data of any victim participant. To achieve this goal, we develop a more effective and efficient gradient matching based method called cos-matching to reconstruct the training data attributes. We evaluate our attacks on a variety of real-world datasets, scenarios, assumptions. Our experiments show that our proposed method achieves better attribute attack performance than most existing baselines.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Lingjuan Lyu
2. Lichao Sun
3. Jiahua Dong
4. Han Yu
5. Philip S. Yu
6. Lixu Wang
7. Zhen Fang
8. Xiao Wang

Citations in the corpus

• Federated Class-Incremental Learning (2022, background): “[34] integrates unsupervised domain adaptation [5, 9, 27, 28, 57] into federated learning framework [14, 31].”
• Privacy and Robustness in Federated Learning: Attacks and Defenses (2020, background): “Rethinking Current Privacy Attacks: There are several inherent weaknesses in current attacks that may limit their applicability in FL [161].”