Yazar "Polat, Hüseyin" için Bildiri Koleksiyonu listeleme
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Achieving Optimal Privacy in Trust-Aware Social Recommender Systems
Dokoohaki, Nima; Kaleli, Cihan; Polat, Hüseyin; Matskin, Mihhail (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
Polat, Hüseyin; Du, Wenliang (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
Polat, Hüseyin; Du, Wenliang (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 ... -
Finding the State Sequence Maximizing P(O, I vertical bar lambda) on Distributed HMMs with Privacy
Renckes, Şahin; Polat, Hüseyin; Oysal, Yusuf (IEEE, 2009)Hidden Markov models (HMMs) are widely used by many applications for forecasting purposes. They are increasingly becoming popular models as part of prediction systems in finance, marketing, bio-informatics, speech recognition, ... -
Finding the state sequence maximizing P(O, I\?) on distributed HMMs with privacy
Renckes, Şahin; Polat, Hüseyin; Oysal, Yusuf (2009)Hidden Markov models (HMMs) are widely used by many applications for forecasting purposes. They are increasingly becoming popular models as part of prediction systems in nance, marketing, bio-informatics, speech recognition, ... -
An Improved Profile-based CF Scheme with Privacy
Bilge, Alper; Polat, Hüseyin (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
Bilge, Alper; Polat, Hüseyin (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 ... -
A novel shilling attack detection method
Bilge, Alper; Özdemir, Zeynep; Polat, Hüseyin (Elsevier Science BV, 2014)Recommender systems provide an impressive way to overcome information overload problem. However, they are vulnerable to profile injection or shilling attacks. Malicious users and/or parties might construct fake profiles ... -
On Binary Similarity Measures for Privacy-Preserving Top-N Recommendations
Bilge, Alper; Kaleli, Cihan; Polat, Hüseyin (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 Discovery of Fake Binary Ratings
Okkalıoğlu, Murat; Koç, Mehmet; Polat, Hüseyin (Assoc Computing Machinery, 2015)Privacy-preserving collaborative filtering methods promise to preserve privacy of individuals. In general, privacy has two aspects, preserving the rating values of users and masking who rated which items. In this study, ... -
On the Privacy of Horizontally Partitioned Binary Data-Based Privacy-Preserving Collaborative Filtering
Okkalıoğlu, Murat; Koç, Mehmet; Polat, Hüseyin (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 ... -
Pcf: Projection-Based Collaborative Filtering
Yakut, İbrahim; Polat, Hüseyin; Koç, Mehmet (Scitepress, 2010)Collaborative filtering (CF) systems are effective solutions for information overload problem while contributing web personalization. Different memory-based algorithms operating over entire data set have been utilized for ... -
Privacy-preserving Eigentaste-based collaborative filtering
Yakut, İbrahim; Polat, Hüseyin (Springer-Verlag Berlin, 2007)With the evolution of e-commerce, privacy is becoming a major concern. Many e-companies employ collaborative filtering (CF) techniques to increase their sales by providing truthful recommendations to customers. Many ... -
Privacy-Preserving Kriging Interpolation on Distributed Data
Tuğrul, Bülent; Polat, Hüseyin (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
Tuğrul, Bülent; Polat, Hüseyin (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
Kaleli, Cihan; Polat, Hüseyin (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 ... -
Providing naive Bayesian classifier-based private recommendations on partitioned data
Kaleli, Cihan; Polat, Hüseyin (Springer-Verlag Berlin, 2007)Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ... -
Providing naïve Bayesian classifier-based private recommendations on partitioned data
Kaleli, Cihan; Polat, Hüseyin (2007)Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy ... -
Providing Private Recommendations on Personal Social Networks
Kaleli, Cihan; Polat, Hüseyin (Springer-Verlag Berlin, 2010)Personal social networks are recently used to offer recommendations. Due to privacy concerns, privacy protection while generating accurate referrals is imperative. Since accuracy and privacy are conflicting goals, providing ... -
Providing private recommendations using naive Bayesian classifier
Kaleli, Cihan; Polat, Hüseyin (Springer-Verlag Berlin, 2007)Today's CF systems fail to protect users' privacy. Without privacy protection, it becomes a challenge to collect sufficient and high quality data for CF. With privacy protection, users feel comfortable to provide more ...