A point-of-interest (POI) recommendation system plays an important role in location-based services (LBS) because it can help people to explore new locations and promote advertisers to launch ads to target users. Exiting POI recommendation methods need users' raw check-in data, which can raise location privacy breaches. Even worse, several privacy-preserving recommendation systems could not utilize the transition pattern in the human movement. To address these problems, we propose Successive Point-of-Interest REcommendation with Local differential privacy (SPIREL) framework. SPIREL employs two types of sources from users' check-in history: a transition pattern between two POIs and visiting counts of POIs. We propose a novel objective function for learning the user-POI and POI-POI relationships simultaneously. We further propose two privacy-preserving mechanisms to train our recommendation system. Experiments using two public datasets demonstrate that SPIREL achieves better POI recommendation quality while preserving stronger privacy for check-in history.
There are several works applying DP LDP on the recommendation system @cite_1 @cite_7 @cite_13 . @cite_1 proposed an objective function perturbation method. In their work, a trusted data curator adds Laplace noises to the objective function so that the factorized item matrix satisfies DP. They also proposed a gradient perturbation method which can preserve the privacy of users' ratings from an untrusted data curator. @cite_7 proposed a probabilistic matrix factorization with personalized differential privacy. They used a random sampling method to satisfy different users' privacy requirements. Then, they applied the objective function perturbation method to obtain the perturbed item matrix. Finally, @cite_13 proposed a new recommendation system under LDP. Specifically, users update their profile vectors locally and submit perturbed gradients in the iterative factorization process. Further, to reduce the error incurred by perturbation, they adopted random projection for dimensionality reduction.
@cite_13 @cite_1 @cite_7
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Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. For this, we develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with i) high dimensionality due to a large number of items and ii) iterative estimation algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a novel binary mechanism based on sampling. We additionally introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate recommendation accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements. Matrix factorization (MF) is a prevailing collaborative filtering method for building recommender systems. It requires users to upload their personal preferences to the recommender for performing MF, which raises serious privacy concerns. This paper proposes a differentially private MF mechanism that can prevent an untrusted recommender from learning any users' ratings or profiles. Our design decouples computations upon users' private data from the recommender to users, and makes the recommender aggregate local results in a privacy-preserving way. It uses the objective perturbation to make sure that the final item profiles satisfy differential privacy and solves the challenge to decompose the noise component for objective perturbation into small pieces that can be determined locally and independently by users. We also propose a third-party based mechanism to reduce noises added in each iteration and adapt our online algorithm to the dynamic setting that allows users to leave and join. The experiments show that our proposal is efficient and introduces acceptable side effects on the precision of results. Probabilistic matrix factorization (PMF) plays a crucial role in recommendation systems. It requires a large amount of user data (such as user shopping records and movie ratings) to predict personal preferences, and thereby provides users high-quality recommendation services, which expose the risk of leakage of user privacy. Differential privacy, as a provable privacy protection framework, has been applied widely to recommendation systems. It is common that different individuals have different levels of privacy requirements on items. However, traditional differential privacy can only provide a uniform level of privacy protection for all users. In this paper, we mainly propose a probabilistic matrix factorization recommendation scheme with personalized differential privacy (PDP-PMF). It aims to meet users' privacy requirements specified at the item-level instead of giving the same level of privacy guarantees for all. We then develop a modified sampling mechanism (with bounded differential privacy) for achieving PDP. We also perform a theoretical analysis of the PDP-PMF scheme and demonstrate the privacy of the PDP-PMF scheme. In addition, we implement the probabilistic matrix factorization schemes both with traditional and with personalized differential privacy (DP-PMF, PDP-PMF) and compare them through a series of experiments. The results show that the PDP-PMF scheme performs well on protecting the privacy of each user and its recommendation quality is much better than the DP-PMF scheme.