A framework for privacy-conducive recommendations
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- A framework for privacy-conducive recommendations
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A privacy-enhancing model for location-based personalized recommendations
To receive personalized recommendation, users of a location-based service (e.g., a Location-Based Social Network, LBSN) have to provide personal information and preferences to the location-based service. However, detailed personal information could be ...
The best privacy defense is a good privacy offense: obfuscating a search engine user's profile
User privacy on the internet is an important and unsolved problem. So far, no sufficient and comprehensive solution has been proposed that helps a user to protect his or her privacy while using the internet. Data are collected and assembled by numerous ...
SECRECSY: A Secure Framework for Enhanced Privacy-Preserving Location Recommendations in Cloud Environment
AbstractThe development of Recommender Systems (RSs) aims to generate recommendations with high quality, and on the other hand, the privacy of the user is not considered as a significant issue. Especially, when the RS utilizes the cloud platform for the ...
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