Differentially Private Neighborhood-Based Recommender Systems - Archive ouverte HAL Access content directly
Conference Papers Year : 2017

Differentially Private Neighborhood-Based Recommender Systems

(1) , (2)
1
2
Jun Wang
  • Function : Author
  • PersonId : 1023830
Qiang Tang
  • Function : Author
  • PersonId : 1023831

Abstract

In this paper, we apply the differential privacy concept to neighborhood-based recommendation methods (NBMs) under a probabilistic framework. We first present a solution, by directly calibrating Laplace noise into the training process, to differential-privately find the maximum a posteriori parameters similarity. Then we connect differential privacy to NBMs by exploiting a recent observation that sampling from the scaled posterior distribution of a Bayesian model results in provably differentially private systems. Our experiments show that both solutions allow promising accuracy with a modest privacy budget, and the second solution yields better accuracy if the sampling asymptotically converges. We also compare our solutions to the recent differentially private matrix factorization (MF) recommender systems, and show that our solutions achieve better accuracy when the privacy budget is reasonably small. This is an interesting result because MF systems often offer better accuracy when differential privacy is not applied.
Fichier principal
Vignette du fichier
449885_1_En_31_Chapter.pdf (741.35 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01649022 , version 1 (27-11-2017)

Licence

Attribution - CC BY 4.0

Identifiers

Cite

Jun Wang, Qiang Tang. Differentially Private Neighborhood-Based Recommender Systems. 32th IFIP International Conference on ICT Systems Security and Privacy Protection (SEC), May 2017, Rome, Italy. pp.459-473, ⟨10.1007/978-3-319-58469-0_31⟩. ⟨hal-01649022⟩
516 View
34 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More