Collaborative-learning based recommender systems emerged following the
success of collaborative learning techniques such as Federated Learning (FL)
and Gossip Learning (GL). In these systems, users participate in the training
of a recommender system while keeping their history of consumed items on their
devices. While these solutions seemed appealing for preserving the privacy of
the participants at a first glance, recent studies have shown that
collaborative learning can be vulnerable to a variety of privacy attacks. In
this paper we propose a novel privacy attack called Community Detection Attack
(CDA), which allows an adversary to discover the members of a community based
on a set of items of her choice (e.g., discovering users interested in LGBT
content). Through experiments on three real recommendation datasets and by
using two state-of-the-art recommendation models, we assess the sensitivity of
an FL-based recommender system as well as two flavors of Gossip Learning-based
recommender systems to CDA. Results show that on all models and all datasets,
the FL setting is more vulnerable to CDA than Gossip settings. We further
evaluated two off-the-shelf mitigation strategies, namely differential privacy
(DP) and a share less policy, which consists in sharing a subset of model
parameters. Results show a better privacy-utility trade-off for the share less
policy compared to DP especially in the Gossip setting.

本文研究基于协作学习的推荐系统，发现当前解决隐私问题的方法容易受到社区检测攻击（Community Detection Attack，CDA）的威胁。通过三个真实的推荐数据集以及两种现代化的推荐模型的实验，研究人员发现，在 FL 和 Gossip Learning 设置中，所有模型和数据集中，FL 设置比 Gossip 设置更容易受到 CDA 攻击。此外，研究还比较了两种解决方案，发现共享策略比差分隐私策略更好地解决了隐私问题。