Differentially Private Data Publishing and Analysis: A Survey <s> Differentially Private Recommender Systems <s> We consider the problem of producing recommendations from collective user behavior while simultaneously providing guarantees of privacy for these users. Specifically, we consider the Netflix Prize data set, and its leading algorithms, adapted to the framework of differential privacy. Unlike prior privacy work concerned with cryptographically securing the computation of recommendations, differential privacy constrains a computation in a way that precludes any inference about the underlying records from its output. Such algorithms necessarily introduce uncertainty--i.e., noise--to computations, trading accuracy for privacy. We find that several of the leading approaches in the Netflix Prize competition can be adapted to provide differential privacy, without significantly degrading their accuracy. To adapt these algorithms, we explicitly factor them into two parts, an aggregation/learning phase that can be performed with differential privacy guarantees, and an individual recommendation phase that uses the learned correlations and an individual's data to provide personalized recommendations. The adaptations are non-trivial, and involve both careful analysis of the per-record sensitivity of the algorithms to calibrate noise, as well as new post-processing steps to mitigate the impact of this noise. We measure the empirical trade-off between accuracy and privacy in these adaptations, and find that we can provide non-trivial formal privacy guarantees while still outperforming the Cinematch baseline Netflix provides. <s> BIB001 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Differentially Private Recommender Systems <s> As a popular technique in recommender systems, Collaborative Filtering (CF) has been the focus of significant attention in recent years, however, its privacy-related issues, especially for the neighborhood-based CF methods, cannot be overlooked. The aim of this study is to address these privacy issues in the context of neighborhood-based CF methods by proposing a Private Neighbor Collaborative Filtering (PNCF) algorithm. This algorithm includes two privacy preserving operations: Private Neighbor Selection and Perturbation. Using the item-based method as an example, Private Neighbor Selection is constructed on the basis of the notion of differential privacy, meaning that neighbors are privately selected for the target item according to its similarities with others. Recommendation-Aware Sensitivity and a re-designed differential privacy mechanism are introduced in this operation to enhance the performance of recommendations. A Perturbation operation then hides the true ratings of selected neighbors by adding Laplace noise. The PNCF algorithm reduces the magnitude of the noise introduced from the traditional differential privacy mechanism. Moreover, a theoretical analysis is provided to show that the proposed algorithm can resist a KNN attack while retaining the accuracy of recommendations. The results from experiments on two real datasets show that the proposed PNCF algorithm can obtain a rigid privacy guarantee without high accuracy loss. <s> BIB002 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Differentially Private Recommender Systems <s> Recommender systems rely on personal information about user behavior for the recommendation generation purposes. Thus, they inherently have the potential to hamper user privacy and disclose sensitive information. Several works studied how neighborhood-based recommendation methods can incorporate user privacy protection. However, privacy preserving latent factor models, in particular, those represented by matrix factorization techniques, the state-of-the-art in recommender systems, have received little attention. In this paper, we address the problem of privacy preserving matrix factorization by utilizing differential privacy, a rigorous and provable approach to privacy in statistical databases. We propose a generic framework and evaluate several ways, in which differential privacy can be applied to matrix factorization. By doing so, we specifically address the privacy-accuracy trade-off offered by each of the algorithms. We show that, of all the algorithms considered, input perturbation results in the best recommendation accuracy, while guaranteeing a solid level of privacy protection against attacks that aim to gain knowledge about either specific user ratings or even the existence of these ratings. Our analysis additionally highlights the system aspects that should be addressed when applying differential privacy in practice, and when considering potential privacy preserving solutions. <s> BIB003 </s> Differentially Private Data Publishing and Analysis: A Survey <s> Differentially Private Recommender Systems <s> Privacy risks of recommender systems have caused increasing attention. Users’ private data is often collected by probably untrusted recommender system in order to provide high-quality recommendation. Meanwhile, malicious attackers may utilize recommendation results to make inferences about other users’ private data. Existing approaches focus either on keeping users’ private data protected during recommendation computation or on preventing the inference of any single user’s data from the recommendation result. However, none is designed for both hiding users’ private data and preventing privacy inference. To achieve this goal, we propose in this paper a hybrid approach for privacy-preserving recommender systems by combining differential privacy (DP) with randomized perturbation (RP). We theoretically show the noise added by RP has limited effect on recommendation accuracy and the noise added by DP can be well controlled based on the sensitivity analysis of functions on the perturbed data. Extensive experiments on three large-scale real world datasets show that the hybrid approach generally provides more privacy protection with acceptable recommendation accuracy loss, and surprisingly sometimes achieves better privacy without sacrificing accuracy, thus validating its feasibility in practice. <s> BIB004
Recommender systems are one of the most popular applications in e-commerce and online social networks. They are capable of recommending products users will probably like. Collaborative filtering (CF) is one of the most popular recommendation techniques as it is insensitive to the product details. However, a risk of potential privacy leaks exist in the recommendation process. For example, continual observation of the recommendations with some background information enables an adversary to infer the individuals rating or purchase history  . McSherry et. al. BIB001 introduced a differential privacy mechanism to traditional recommender algorithms, adding Laplace noise into the covariance matrix. Zhu et al. BIB002 proposed a private k nearest neighbor (KNN) algorithm for neighborhood-based recommender systems while minimizing the accuracy loss of the recommendations. The algorithm can resist attacks on collaborative filtering and has the potential to be extended to other scenarios that need to perform top-k private selection from candidates, such as top-k queries. Friedman et al. BIB003 presented a differentially private SVD algorithm to deal with the privacy problem in matrix factorization based recommender systems. The majority of work on private recommender systems assume attackers are either public users or recommender servers. Liu et al. BIB004 proposed a hybrid approach which combines differential privacy with randomized perturbation not only to hide users private data from servers but also prevent privacy inference from pubic users. In practice, randomized perturbation can sometime improve the accuracy of the recommendations.