Privacy-preserving Collaborative Filtering Using Randomized Response
نویسندگان
چکیده
منابع مشابه
Privacy-Preserving Collaborative Filtering Using Randomized Perturbation Techniques
Collaborative Filtering (CF) techniques are becoming increasingly popular with the evolution of the Internet. E-commerce sites use CF systems to suggest products to customers based on like-minded customers’ preferences. People use CF systems to cope with information overload. To conduct collaborative filtering, data from customers are needed. However, collecting high quality data from customers...
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Collaborative filtering (CF) techniques are becoming very popular on the Internet and are widely used in several domains to cope with information overload. E-commerce sites use filtering systems to recommend products to customers based on the preferences of like-minded customers, but their systems do not protect user privacy. Because users concerned about privacy may give false information, it ...
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Collaborative filtering is a famous technique in recommendation systems. Yet, it requires the users to reveal their preferences, which has undesirable privacy implications. Over the years, researchers have proposed many privacy-preserving collaborative filtering (PPCF) systems using very different techniques for different settings, ranging from adding noise to the data with centralized filterin...
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Although collaborative filtering with privacy schemes protect individual user privacy while still providing accurate recommendations, they might be subject to shilling attacks like traditional schemes without privacy. There are various studies focusing on either proposing privacypreserving collaborative filtering schemes or developing robust recommendation algorithms against shilling attacks. H...
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ژورنال
عنوان ژورنال: Journal of Information Processing
سال: 2013
ISSN: 1882-6652
DOI: 10.2197/ipsjjip.21.617