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Toplam kayıt 12, 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, ...
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 ...
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 ...
On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative Filtering
(Springer Int Publishing Ag, 2016)
Collaborative filtering systems provide recommendations for their users. Privacy is not a primary concern in these systems; however, it is an important element for the true user participation. Privacy-preserving collaborative ...
On Binary Similarity Measures for Privacy-Preserving Top-N Recommendations
(Scitepress, 2010)
Collaborative filtering (CF) algorithms fundamentally depend on similarities between users and/or items to predict individual preferences. There are various binary similarity measures like Kulzinslcy, Sokal-Michener, Yule, ...
Privacy-Preserving Trust-based Recommendations on Vertically Distributed Data
(IEEE Computer Soc, 2011)
Providing recommendations on trusts between entities is receiving increasing attention lately. Customers may prefer different online vendors for shopping. Thus, their preferences about various products might be distributed ...