Recommender Systems with Condensed Local Differential Privacy

Recommender systems aim at predicting users’ future behaviors by learning the users’ personal information and historical behaviors. Unfortunately, training by the user’s raw data will inevitably cause the user’s private information to leak. Moreover, exiting privacy-preserving recommendation methods are based on matrix factorization with Local Differential Privacy (LDP). Because LDP performs poorly in small or multi-dimensional data sets, these methods do not work well with sparse or multi-dimensional features. Motivated by these questions, we propose a privacy-preserving recommendation model based on Deep neural networks and Factorization Machines (DeepFM), which can provide users with recommendations under the promise of protecting users’ privacy. In our recommendation model, each user randomly perturbs the calculated gradients to satisfy Condensed Local Differential Privacy (CLDP). The recommender system collects the perturbed gradients to train a recommendation model, which guarantees the safety of users’ private information and has excellent accuracy for the recommendation. Experiments on a real-world dataset show that the recommendation accuracy of our algorithm performs better than the existing methods.