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Toplam kayıt 15, listelenen: 1-10
Privacy-Preserving Kriging Interpolation on Distributed Data
(Springer-Verlag Berlin, 2014)
Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ...
Effects of Inconsistently Masked Data Using RPT on CF with Privacy
(Assoc Computing Machinery, 2007)
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed ...
Privacy Risks for Multi-Criteria Collaborative Filtering Systems
(IEEE, 2017)
In case that individuals feel their privacy is violated while using any recommender system, they might be willing to declare incorrect information or even completely refuse to use such services. To relieve customer concerns, ...
Privacy-preserving kriging interpolation on distributed data
(Springer Verlag, 2014)
Kriging is one of the most preferred geostatistical methods in many engineering fields. Basically, it creates a model using statistical properties of all measured points in the region, where a prediction value is sought. ...
Methods of Privacy Preserving in Collaborative Filtering
(IEEE, 2017)
Privacy considerations of individuals becomes more and more popular issue in recommender systems due to the increasing need for protecting confidential data. Even though users of recommender systems enjoy with personalized ...
An Improved Profile-based CF Scheme with Privacy
(IEEE Computer Soc, 2011)
Traditional collaborative filtering (CF) systems widely employing k- nearest neighbor (kNN) algorithms mostly attempt to alleviate the contemporary problem of information overload by generating personalized predictions for ...
Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
(Springer-Verlag Berlin, 2010)
Collaborative filtering (CF) recommenders are subject to numerous shortcomings such as centralized processing, vulnerability to shilling attacks, and most important of all privacy. To overcome these obstacles, researchers ...
Effects of inconsistently masked data using RPT on CF with privacy
(2007)
Randomized perturbation techniques (RPT) are applied to perturb the customers' private data to protect privacy while providing accurate referrals. In the RPT-based collaborative filtering (CF) with privacy schemes, proposed ...
Improving privacy-preserving NBC-based recommendations by preprocessing
(2010)
Providing accurate predictions efficiently with privacy is imperative for both customers and e-commerce vendors. However, privacy, accuracy, and performance are conflicting goals. Although producing referrals with privacy ...
Privacy-Preserving Concordance-based Recommendations on Vertically Distributed Data
(IEEE, 2012)
Recommender systems are attractive components of e-commerce. Customers apply such systems to get help for choosing the appropriate product to purchase. To provide accurate and dependable referrals, recommender systems ...