2,336 research outputs found
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
On content-based recommendation and user privacy in social-tagging systems
Recommendation systems and content filtering approaches based on annotations and ratings, essentially rely on users expressing their preferences and interests through their actions, in order to provide personalised content. This activity, in which users engage collectively has been named social tagging, and it is one of the most popular in which users engage online, and although it has opened new possibilities for application interoperability on the semantic web, it is also posing new privacy threats. It, in fact, consists of describing online or offline resources by using free-text labels (i.e. tags), therefore exposing the user profile and activity to privacy attacks. Users, as a result, may wish to adopt a privacy-enhancing strategy in order not to reveal their interests completely. Tag forgery is a privacy enhancing technology consisting of generating tags for categories or resources that do not reflect the user's actual preferences. By modifying their profile, tag forgery may have a negative impact on the quality of the recommendation system, thus protecting user privacy to a certain extent but at the expenses of utility loss. The impact of tag forgery on content-based recommendation is, therefore, investigated in a real-world application scenario where different forgery strategies are evaluated, and the consequent loss in utility is measured and compared.Peer ReviewedPostprint (author’s final draft
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 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 NN attack. We define the sum of
neighbours' similarity as the accuracy metric alpha, the number of user
partitions, across which we select the neighbours, as the security metric
beta. We generalise the 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 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
User's Privacy in Recommendation Systems Applying Online Social Network Data, A Survey and Taxonomy
Recommender systems have become an integral part of many social networks and
extract knowledge from a user's personal and sensitive data both explicitly,
with the user's knowledge, and implicitly. This trend has created major privacy
concerns as users are mostly unaware of what data and how much data is being
used and how securely it is used. In this context, several works have been done
to address privacy concerns for usage in online social network data and by
recommender systems. This paper surveys the main privacy concerns, measurements
and privacy-preserving techniques used in large-scale online social networks
and recommender systems. It is based on historical works on security,
privacy-preserving, statistical modeling, and datasets to provide an overview
of the technical difficulties and problems associated with privacy preserving
in online social networks.Comment: 26 pages, IET book chapter on big data recommender system
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