Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/87023
Title: Privacy enhanced matrix factorization for recommendation with local differential privacy
Authors: Shin, Hyejin
Kim, Sungwook
Shin, Junbum
Xiao, Xiaokui
Keywords: Matrix Factorization
Local Differential Privacy
Issue Date: 2018
Source: Shin, H., Kim, S., Shin, J., & Xiao, X. Privacy Enhanced Matrix Factorization for Recommendation with Local Differential Privacy. IEEE Transactions on Knowledge and Data Engineering, in press.
Series/Report no.: IEEE Transactions on Knowledge and Data Engineering
Abstract: 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 systems, but the existing solutions in the literature can protect either items or ratings only. In this paper, we propose a recommender system that protects both user's items and ratings. We develop novel matrix factorization algorithms under local differential privacy (LDP). In a recommender system with LDP, individual users randomize their data themselves to satisfy differential privacy and send the perturbed data to the recommender. Then, the recommender computes aggregates of the perturbed data. This framework ensures that both user's items and ratings remain private from the recommender. However, applying LDP to matrix factorization typically raises utility issues with high dimensionality and iterative algorithms. To tackle these technical challenges, we adopt dimensionality reduction technique and a sampling-based binary mechanism. We introduce a factor that stabilizes the perturbed gradients. With MovieLens and LibimSeTi datasets, we evaluate accuracy of our recommender system and demonstrate that our algorithm performs better than the existing differentially private gradient descent algorithm for matrix factorization under stronger privacy requirements.
URI: https://hdl.handle.net/10356/87023
http://hdl.handle.net/10220/45218
ISSN: 1041-4347
DOI: 10.1109/TKDE.2018.2805356
Rights: © 2018 IEEE.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:SCSE Journal Articles

SCOPUSTM   
Citations 5

86
Updated on Jul 9, 2022

Web of ScienceTM
Citations 5

60
Updated on Jul 4, 2022

Page view(s)

349
Updated on Sep 29, 2022

Google ScholarTM

Check

Altmetric


Plumx

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.