Related papers: Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios

Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios

URL: http://arxiv.org/abs/2204.13594v1
Date: Tue, 26 Apr 2022 15:23:05 GMT
Title: Poisoning Deep Learning based Recommender Model in Federated Learning Scenarios
Authors: Dazhong Rong, Qinming He, Jianhai Chen
Abstract summary: We design attack approaches targeting deep learning based recommender models in federated learning scenarios. Our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.
Score: 7.409990425668484
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Various attack methods against recommender systems have been proposed in the past years, and the security issues of recommender systems have drawn considerable attention. Traditional attacks attempt to make target items recommended to as many users as possible by poisoning the training data. Benifiting from the feature of protecting users' private data, federated recommendation can effectively defend such attacks. Therefore, quite a few works have devoted themselves to developing federated recommender systems. For proving current federated recommendation is still vulnerable, in this work we probe to design attack approaches targeting deep learning based recommender models in federated learning scenarios. Specifically, our attacks generate poisoned gradients for manipulated malicious users to upload based on two strategies (i.e., random approximation and hard user mining). Extensive experiments show that our well-designed attacks can effectively poison the target models, and the attack effectiveness sets the state-of-the-art.

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