Konu "Privacy" için Bildiri Koleksiyonu listeleme
Toplam kayıt 16, listelenen: 1-16
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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
(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 ... -
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 ... -
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 ... -
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 ... -
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 ... -
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, ... -
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 ... -
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 Collaborative Filtering on Overlapped Ratings
(IEEE Computer Soc, 2013)To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable ... -
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 ... -
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. ... -
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. ... -
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 ... -
Randomization-based Privacy-preserving Frameworks for Collaborative Filtering
(Elsevier Science BV, 2016)Randomization-based privacy protection methods are widely used in collaborative filtering systems to achieve individual privacy. The basic idea behind randomization utilized in collaborative filtering schemes is to add ... -
Similar or Dissimilar Users? Or Both?
(IEEE Computer Soc, 2009)E-commerce sites utilize collaborative filtering (CF) techniques to offer recommendations to their customers. To recruit new customers and keep the current ones, it is imperative for online vendors to provide accurate ...