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Differentially Private Neighborhood-based Recommender Systems
Wang, Jun; Tang, Qiang
2017In IFIP Information Security & Privacy Conference
Peer reviewed
 

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Keywords :
Differential Privacy; Recommender Systems
Abstract :
[en] Privacy issues of recommender systems have become a hot topic for the society as such systems are appearing in every corner of our life. In contrast to the fact that many secure multi-party computation protocols have been proposed to prevent information leakage in the process of recommendation computation, very little has been done to restrict the information leakage from the recommendation results. 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.
Disciplines :
Computer science
Author, co-author :
Wang, Jun ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Tang, Qiang
External co-authors :
no
Language :
English
Title :
Differentially Private Neighborhood-based Recommender Systems
Alternative titles :
[en] Differentially Private Neighborhood-based Recommender Systems
Publication date :
May 2017
Event name :
32nd IFIP Information Security & Privacy Conference
Event organizer :
IFIP Information Security & Privacy Conference
Event place :
Rome, Italy
Event date :
from 5-29-2017 to 31-5-2017
Audience :
International
Main work title :
IFIP Information Security & Privacy Conference
Publisher :
Springer
Pages :
14
Peer reviewed :
Peer reviewed
Focus Area :
Computational Sciences
Commentary :
Best Student Paper award nominated
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since 13 March 2017

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