An Accuracy-Assured Privacy-Preserving Recommender System for Internet Commerce

Recommender systems, tool for predicting users' potential preferences by computing history data and users' interests, show an increasing importance in various Internet applications such as online shopping. As a well-known recommendation method, neighbourhood-based collaborative filtering has attracted considerable attention recently. The risk of revealing users' private information during the process of filtering has attracted noticeable research interests. Among the current solutions, the probabilistic techniques have shown a powerful privacy preserving effect. When facing $k$ Nearest Neighbour attack, all the existing methods provide no data utility guarantee, for the introduction of global randomness. In this paper, to overcome the problem of recommendation accuracy loss, we propose a novel approach, Partitioned Probabilistic Neighbour Selection, to ensure a required prediction accuracy while maintaining high security against $k$NN attack. We define the sum of $k$ neighbours' similarity as the accuracy metric alpha, the number of user partitions, across which we select the $k$ neighbours, as the security metric beta. We generalise the $k$ Nearest Neighbour attack to beta k Nearest Neighbours attack. Differing from the existing approach that selects neighbours across the entire candidate list randomly, our method selects neighbours from each exclusive partition of size $k$ with a decreasing probability. Theoretical and experimental analysis show that to provide an accuracy-assured recommendation, our Partitioned Probabilistic Neighbour Selection method yields a better trade-off between the recommendation accuracy and system security.Comment: replacement for the previous versio

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

Differentially Private Neighborhood-based Recommender Systems

Privacy issues of recommender systems have become a hot topic for the society as such systems are appearing in every corner of our life. In contrast to the fact that many secure multi-party computation protocols have been proposed to prevent information leakage in the process of recommendation computation, very little has been done to restrict the information leakage from the recommendation results. In this paper, we apply the differential privacy concept to neighborhood-based recommendation methods (NBMs) under a probabilistic framework. We first present a solution, by directly calibrating Laplace noise into the training process, to differential-privately find the maximum a posteriori parameters similarity. Then we connect differential privacy to NBMs by exploiting a recent observation that sampling from the scaled posterior distribution of a Bayesian model results in provably differentially private systems. Our experiments show that both solutions allow promising accuracy with a modest privacy budget, and the second solution yields better accuracy if the sampling asymptotically converges. We also compare our solutions to the recent differentially private matrix factorization (MF) recommender systems, and show that our solutions achieve better accuracy when the privacy budget is reasonably small. This is an interesting result because MF systems often offer better accuracy when differential privacy is not applied

Paper Survey And Example Of Collaborative Filtering Implementation In Recommender System

The development of recommender system research has expanded to various applications. Recommender system issues can be analyzed from many perspectives such as user rating strategy, user preferences and text mining. User rating strategy and user preferences are associated with user behavior to find suitable recommended items. Text mining is considered the most related field to database management and web search queries. The relation to the database query, it needs suitable query algorithm web search and user profiling strategy. Our paper survey showed that Latent Semantic Analysis (LSA) method has a better chance to solve recommender system issues especially in web search and user profiling. By comparing with restaurant samples, we describe adequate measures to evaluate the recommender system quality in user profiling. Some algorithm can provide benefits to improve the quality of personalized recommendations that are tailored to user attributes. Further research can provide newer algorithm to handle cold start problem and sparse data from both text mining and mining computation perspectives

Improved collaborative filtering using clustering and association rule mining on implicit data

The recommender systems are recently becoming more significant due to their ability in making decisions on appropriate choices. Collaborative Filtering (CF) is the most successful and most applied technique in the design of a recommender system where items to an active user will be recommended based on the past rating records from like-minded users. Unfortunately, CF may lead to poor recommendation when user ratings on items are very sparse (insufficient number of ratings) in comparison with the huge number of users and items in user-item matrix. In the case of a lack of user rating on items, implicit feedback is used to profile a user’s item preferences. Implicit feedback can indicate users’ preferences by providing more evidences and information through observations made on users’ behaviors. Data mining technique, which is the focus of this research, can predict a user’s future behavior without item evaluation and can too, analyze his preferences. In order to investigate the states of research in CF and implicit feedback, a systematic literature review has been conducted on the published studies related to topic areas in CF and implicit feedback. To investigate users’ activities that influence the recommender system developed based on the CF technique, a critical observation on the public recommendation datasets has been carried out. To overcome data sparsity problem, this research applies users’ implicit interaction records with items to efficiently process massive data by employing association rules mining (Apriori algorithm). It uses item repetition within a transaction as an input for association rules mining, in which can achieve high recommendation accuracy. To do this, a modified preprocessing has been employed to discover similar interest patterns among users. In addition, the clustering technique (Hierarchical clustering) has been used to reduce the size of data and dimensionality of the item space as the performance of association rules mining. Then, similarities between items based on their features have been computed to make recommendations. Experiments have been conducted and the results have been compared with basic CF and other extended version of CF techniques including K-Means Clustering, Hybrid Representation, and Probabilistic Learning by using public dataset, namely, Million Song dataset. The experimental results demonstrate that the proposed technique exhibits improvements of an average of 20% in terms of Precision, Recall and Fmeasure metrics when compared to the basic CF technique. Our technique achieves even better performance (an average of 15% improvement in terms of Precision and Recall metrics) when compared to the other extended version of CF techniques, even when the data is very sparse

An effective privacy preserving algorithm for neighborhood-based collaborative filtering

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. © 2013 Published by Elsevier B.V. All rights reserved

Intelligent techniques for recommender systems

This thesis focuses on the data sparsity issue and the temporal dynamic issue in the context of collaborative filtering, and addresses them with imputation techniques, low-rank subspace techniques and optimizations techniques from the machine learning perspective. A comprehensive survey on the development of collaborative filtering techniques is also included

Privacy preserving recommender systems

The recommender systems help users find suitable and interesting products and contents from the huge amount of information that are available in the internet. There are various types of recommender systems available which have been providing recommendation services to users. For example Collaborative Filtering (CF) based recommendations, Content based (CB) recommendations, context aware recommendations and so on. Despite the fact that these recommender systems are very useful to solve the information overload problem by filtering interesting information, they suffer from huge privacy issues. In order to generate user personalized recommendations, the recommendation service providers need to acquire the information related to attributes, preferences, experiences as well as demands, which are related to users' confidential information. Usually the more information available to the service providers, the more accurate recommendations can be generated. However, the service providers are not always trustworthy to share personal information for recommendation purposes since they may cause serious privacy threats to users' privacy by leaking them to other parties or providing false recommendations. Therefore the user information must be protected prior to share them to any third party service provider to ensure the privacy of users. To overcome the privacy issues of recommender systems several techniques have been proposed which can be categorized into decentralization, randomization and secure computations based approaches. In decentralization based approach, the central service providers are removed and the main controls of recommendation services are given to participant users. The main issue with this kind of approach is that to generate recommendations, the users need to be dependant to other users' availability in online services. If any user becomes offline, her information can not be used in the system. The randomization based techniques add noises to users data to obfuscate them from learning the true information. However the main issue is that adding noise affects recommendation accuracy. On the contrary, the secure computations preserve user information while providing accurate recommendations. In this thesis we preserve user privacy by means of encrypting user information, specifically their ratings and other related information using homomorphic encryption based techniques to provide recommendations based on the encrypted data. The main advantage of homomorphic encryption based technique is that it is semantically secure and computationally it is hard to distinguish the true information from the given ciphertext. Using the homomorphic based encryption tools and techniques we build different privacy preserving protocols for different types of recommendation approaches by analyzing their privacy requirements and challenges. More specifically, we focus on different key recommendation techniques and differentiate them into centralized and partitioned dataset based recommendation techniques. From available recommendation techniques, we found that some of the existing and popular recommendation techniques like user based recommendation, item based recommendation and context aware recommendation can be grouped into centralized recommendation approach. In partitioned dataset based recommendation, the user information can be partitioned into different organizations and these organizations can collaborate with each other by gathering sufficient information in order to provide accurate recommendations without revealing their own confidential information. After categorizing the recommendation techniques we analyze the problems and requirements in terms of privacy preservation. Then for each type of recommendation approach, we develop the privacy preserving protocols to generate recommendations taking their specific privacy requirements and challenges into consideration. We also investigate the problems and limitations of existing privacy preserving recommendations and found that the current solutions suffer from huge computation and communication overhead as well as privacy of users. In the thesis we identify the related problems and solve the issues using our proposed privacy preserving protocols. As an overall idea, our proposed recommendation protocols work as follows. The users encrypt their ratings using homomorphic encryption and send them to service providers. We assume the service providers are semi honest but curious, they follow the protocol but at the same time try to find new information from the available data. The service provider has the ability to perform homomorphic operations and it performs certain computations over encrypted data without learning any true information and returns the results to the query users who ask for recommendations. The system models of our privacy preserving protocols for different recommendation techniques differ from each other because of their different privacy requirements. The proposed privacy preserving protocols are tested on various real world datasets. Based on the application areas of different recommendation approaches our gathered datasets are also different such as movie rating, social network, checkin information for different locations and quality of service of web services. For each proposed privacy preserving protocols we also present the privacy analysis and describe how the system can perform the computations without leaking the private information of users. The experimental and privacy analysis of our proposed privacy preserving protocols for different types of recommendation techniques show that they are private as well as practical