  Recommender systems are widely used. Usually, recommender systems are based
on a centralized client-server architecture. However, this approach implies
drawbacks regarding the privacy of users. In this paper, we propose a
distributed reciprocal recommender system with strong, self-determined privacy
guarantees, i.e., local differential privacy. More precisely, users randomize
their profiles locally and exchange them via a peer-to-peer network.
Recommendations are then computed and ranked locally by estimating similarities
between profiles. We evaluate recommendation accuracy of a job recommender
system and demonstrate that our method provides acceptable utility under strong
privacy requirements.
