  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 maintaining their history of consumed items on
their devices. While these solutions seemed appealing for preserving the
privacy of the participants at first glance, recent studies have revealed that
collaborative learning can be vulnerable to various privacy attacks. In this
paper, we study the resilience of collaborative learning-based recommender
systems against a novel privacy attack called Community Detection Attack (CDA).
This attack enables an adversary to identify community members based on a
chosen set of items (eg., identifying users interested in specific
points-of-interest). Through experiments on three real recommendation datasets
using two state-of-the-art recommendation models, we evaluate the sensitivity
of an FL-based recommender system as well as two flavors of Gossip
Learning-based recommender systems to CDA. The results show that across all
models and datasets, the FL setting is more vulnerable to CDA compared to
Gossip settings. Furthermore, we assess two off-the-shelf mitigation
strategies, namely differential privacy (DP) and a \emph{Share less} policy,
which consists of sharing a subset of less sensitive model parameters. The
findings indicate a more favorable privacy-utility trade-off for the
\emph{Share less} strategy, particularly in FedRecs.
