A method for privacy-preserving collaborative filtering recommendations

With the continuous growth of the Internet and the progress of electronic commerce the issues of product recommendation and privacy protection are becoming increasingly important. Recommender Systems aim to solve the information overload problem by providing accurate recommendations of items to users. Collaborative filtering is considered the most widely used recommendation method for providing recommendations of items or users to other users in online environments. Additionally, collaborative filtering methods can be used with a trust network, thus delivering to the user recommendations from both a database of ratings and from users who the person who made the request knows and trusts. On the other hand, the users are having privacy concerns and are not willing to submit the required information (e.g., ratings for products), thus making the recommender system unusable. In this paper, we propose (a) an approach to product recommendation that is based on collaborative filtering and uses a combination of a ratings network with a trust network of the user to provide recommendations and (b) “neighbourhood privacy” that employs a modified privacy-aware role-based access control model that can be applied to databases that utilize recommender systems. Our proposed approach (1) protects user privacy with a small decrease in the accuracy of the recommendations and (2) uses information from the trust network to increase the accuracy of the recommendations, while, (3) providing privacy-preserving recommendations, as accurate as the recommendations provided without the privacy-preserving approach or the method that increased the accuracy applied

Vertical Federated Graph Neural Network for Recommender System

Conventional recommender systems are required to train the recommendation model using a centralized database. However, due to data privacy concerns, this is often impractical when multi-parties are involved in recommender system training. Federated learning appears as an excellent solution to the data isolation and privacy problem. Recently, Graph neural network (GNN) is becoming a promising approach for federated recommender systems. However, a key challenge is to conduct embedding propagation while preserving the privacy of the graph structure. Few studies have been conducted on the federated GNN-based recommender system. Our study proposes the first vertical federated GNN-based recommender system, called VerFedGNN. We design a framework to transmit: (i) the summation of neighbor embeddings using random projection, and (ii) gradients of public parameter perturbed by ternary quantization mechanism. Empirical studies show that VerFedGNN has competitive prediction accuracy with existing privacy preserving GNN frameworks while enhanced privacy protection for users' interaction information.Comment: 17 pages, 9 figure

Guiding Us Throughout a Sea of Data - A Survey on Recommender Systems and Its Privacy Challenges

Over the past decades, the Internet has served as the backbone connecting people to others, places and things. With the sheer volume of information generated everyday, people can feel overwhelmed when having to make a selection among the multiple options that normally come up after a search or application request. For example, when searching for news articles regarding a particular topic, the search engine will present a number of results to you. When looking for some product on shopping websites, there are usually several pages of results that match the keywords. It can be very challenging for people to find their most expected information in the era of big data. A recommender system is a program that utilizes algorithms to learn users’ preferences from historical data, and predict their future interests. Recommender systems are employed everywhere in the cyberspace. Many websites including Amazon, eBay, YouTube, Facebook, Netflix, and others, have integrated automatic personalized recommendation techniques into their systems, in order to help users find their most desired information. While recommender systems have become a common feature on most web applications and sites, one of the major issues around its use is privacy concerns. A regular recommender system requires the users to share their online behavior data, such as their past shopping records, browsing history, visited places, so that it can learn their preferences. This can potentially deter people from using the system because these data are considered as users’ privacy and many do not feel comfortable sharing the information with other parties. In this research, we studied several recommendation algorithms, and compared their performance as well as prediction accuracy on real-world datasets. We also proposed a novel nonnegative matrix factorization (NMF) based privacy-preserving point-of-interest recommendation framework, in which the latent factors in NMF are learned on user group preference instead of individual user preference. Recommendations are made by personalizing the group preference on user’s local devices. There are no location or check-in data collected from the users anywhere throughout the learning and recommendation processes. Some preliminary results on a regular recommender system were established and two GUI applications were developed. The on-going research focuses on integrating the privacy-preserving framework into the system and verifying the effectiveness as well as the recommendation accuracy of the proposed model

Privacy-preserving collaboration in an integrated social environment

Privacy and security of data have been a critical concern at the state, organization and individual levels since times immemorial. New and innovative methods for data storage, retrieval and analysis have given rise to greater challenges on these fronts. Online social networks (OSNs) are at the forefront of individual privacy concerns due to their ubiquity, popularity and possession of a large collection of users' personal data. These OSNs use recommender systems along with their integration partners (IPs) for offering an enriching user experience and growth. However, the recommender systems provided by these OSNs inadvertently leak private user information. In this work, we develop solutions targeted at addressing existing, real-world privacy issues for recommender systems that are deployed across multiple OSNs. Specifically, we identify the various ways through which privacy leaks can occur in a friend recommendation system (FRS), and propose a comprehensive solution that integrates both Differential Privacy and Secure Multi-Party Computation (MPC) to provide a holistic privacy guarantee. We model a privacy-preserving similarity computation framework and library named Lucene-P2. It includes the efficient privacy-preserving Latent Semantic Indexing (LSI) extension. OSNs can use the Lucene-P2 framework to evaluate similarity scores for their private inputs without sharing them. Security proofs are provided under semi-honest and malicious adversary models. We analyze the computation and communication complexities of the protocols proposed and empirically test them on real-world datasets. These solutions provide functional efficiency and data utility for practical applications to an extent.Includes bibliographical references

Data Privacy Preservation in Collaborative Filtering Based Recommender Systems

This dissertation studies data privacy preservation in collaborative filtering based recommender systems and proposes several collaborative filtering models that aim at preserving user privacy from different perspectives. The empirical study on multiple classical recommendation algorithms presents the basic idea of the models and explores their performance on real world datasets. The algorithms that are investigated in this study include a popularity based model, an item similarity based model, a singular value decomposition based model, and a bipartite graph model. Top-N recommendations are evaluated to examine the prediction accuracy. It is apparent that with more customers\u27 preference data, recommender systems can better profile customers\u27 shopping patterns which in turn produces product recommendations with higher accuracy. The precautions should be taken to address the privacy issues that arise during data sharing between two vendors. Study shows that matrix factorization techniques are ideal choices for data privacy preservation by their nature. In this dissertation, singular value decomposition (SVD) and nonnegative matrix factorization (NMF) are adopted as the fundamental techniques for collaborative filtering to make privacy-preserving recommendations. The proposed SVD based model utilizes missing value imputation, randomization technique, and the truncated SVD to perturb the raw rating data. The NMF based models, namely iAux-NMF and iCluster-NMF, take into account the auxiliary information of users and items to help missing value imputation and privacy preservation. Additionally, these models support efficient incremental data update as well. A good number of online vendors allow people to leave their feedback on products. It is considered as users\u27 public preferences. However, due to the connections between users\u27 public and private preferences, if a recommender system fails to distinguish real customers from attackers, the private preferences of real customers can be exposed. This dissertation addresses an attack model in which an attacker holds real customers\u27 partial ratings and tries to obtain their private preferences by cheating recommender systems. To resolve this problem, trustworthiness information is incorporated into NMF based collaborative filtering techniques to detect the attackers and make reasonably different recommendations to the normal users and the attackers. By doing so, users\u27 private preferences can be effectively protected