Local Differentially Private Matrix Factorization with MoG for Recommendations

Jeyamohan Neera*, Xiaomin Chen, Nauman Aslam, Zhan Shu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Citations (Scopus)
45 Downloads (Pure)


Unethical data aggregation practices of many recommendation systems have raised privacy concerns among users. Local differential privacy (LDP) based recommendation systems address this problem by perturbing a user’s original data locally in their device before sending it to the data aggregator (DA). The DA performs recommendations over perturbed data which causes substantial prediction error. To tackle privacy and utility issues with untrustworthy DA in recommendation systems, we propose a novel LDP matrix factorization (MF) with mixture of Gaussian (MoG). We use a Bounded Laplace mechanism (BLP) to perturb user’s original ratings locally. BLP restricts the perturbed ratings to a predefined output domain, thus reducing the level of noise aggregated at DA. The MoG method estimates the noise added to the original ratings, which further improves the prediction accuracy without violating the principles of differential privacy (DP). With Movielens and Jester datasets, we demonstrate that our method offers a higher prediction accuracy under strong privacy protection compared to existing LDP recommendation methods.
Original languageEnglish
Title of host publicationData and Applications Security and Privacy XXXIV
Subtitle of host publication34th Annual IFIP WG 11.3 Conference, DBSec 2020, Regensburg, Germany, June 25–26, 2020, Proceedings
EditorsAnoop Singhal, Jaideep Vaidya
Place of PublicationCham
Number of pages13
ISBN (Electronic)9783030496692
ISBN (Print)9783030496685
Publication statusPublished - 2020
Event34th Annual IFIP WG 11.3 Conference: DBSec 2020 - Virtual, Regensburg, Germany
Duration: 25 Jun 202026 Jun 2020

Publication series

NameLecture Notes in Computer Science


Conference34th Annual IFIP WG 11.3 Conference
Internet address


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