Practical Attribute Reconstruction Attack Against Federated Learning

Existing federated learning (FL) designs have been shown to exhibit vulnerabilities which can be exploited by adversaries to compromise data privacy. However, most current works conduct attacks by leveraging gradients calculated on a small batch of data. This setting is not realistic as gradients are normally shared after at least 1 epoch of local training on each participant's local data in FL for communication efficiency. In this work, we conduct a unique systematic evaluation of attribute reconstruction attack (ARA) launched by the malicious server in the FL system, and empirically demonstrate that the shared local model gradients after 1 epoch of local training can still reveal sensitive attributes of local training data. To demonstrate this leakage, we develop a more effective and efficient gradient matching based method called cos-matching to reconstruct the sensitive attributes of any victim participant's training data. Based on the reconstructed training data attributes, we further show that an attacker can even reconstruct the sensitive attributes of any records that are not included in any participant's training data, thus opening a new attack surface in FL. Extensive experiments show that the proposed method achieves better attribute attack performance than existing state-of-the-art methods.