Privacy enhanced matrix factorization for recommendation with local differential privacy

Privacy enhanced matrix factorization for recommendation with local differential privacy

Recommender systems are collecting and analyzing user data to provide better user experience. However, several privacy concerns have been raised when a recommender knows user's set of items or their ratings. A number of solutions have been suggested to improve privacy of legacy recommender syst...

Full description

Saved in:
Bibliographic Details
Main Authors: Shin, Hyejin, Kim, Sungwook, Shin, Junbum, Xiao, Xiaokui
Other Authors: School of Computer Science and Engineering
Format: Article
Language:English
Published: 2018
Subjects:
Matrix Factorization
Local Differential Privacy
Online Access:https://hdl.handle.net/10356/87023
http://hdl.handle.net/10220/45218
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English

Internet

https://hdl.handle.net/10356/87023
http://hdl.handle.net/10220/45218

Similar Items