NDLI: Privacy-Preserving Personalized Recommendation: An Instance-Based Approach via Differential Privacy

Content Provider	IEEE Xplore Digital Library
Author	Shen, Yilin Jin, Hongxia
Copyright Year	2014
Abstract	Recommender systems become increasingly popular and widely applied nowadays. The release of users' private data is required to provide users accurate recommendations, yet this has been shown to put users at risk. Unfortunately, existing privacy-preserving methods are either developed under trusted server settings with impractical private recommender systems or lack of strong privacy guarantees. In this paper, we develop the first lightweight and provably private solution for personalized recommendation, under untrusted server settings. In this novel setting, users' private data is obfuscated before leaving their private devices, giving users greater control on their data and service providers less responsibility on privacy protections. More importantly, our approach enables the existing recommender systems (with no changes needed) to directly use perturbed data, rendering our solution very desirable in practice. We develop our data perturbation approach on differential privacy, the state-of-the-art privacy model with lightweight computation and strong but provable privacy guarantees. In order to achieve useful and feasible perturbations, we first design a novel relaxed admissible mechanism enabling the injection of flexible instance-based noises. Using this novel mechanism, our data perturbation approach, incorporating the noise calibration and learning techniques, obtains perturbed user data with both theoretical privacy and utility guarantees. Our empirical evaluation on large-scale real-world datasets not only shows its high recommendation accuracy but also illustrates the negligible computational overhead on both personal computers and smart phones. As such, we are able to meet two contradictory goals, privacy preservation and recommendation accuracy. This practical technology helps to gain user adoption with strong privacy protection and benefit companies with high-quality personalized services on perturbed user data.
Starting Page	540
Ending Page	549
File Size	462246
Page Count	10
File Format	PDF
ISSN	15504786
e-ISBN	9781479943029
DOI	10.1109/ICDM.2014.140
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-12-14
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Noise Privacy Data privacy Sensitivity Servers Aggregates Vectors Learning and Optimization Differential Privacy Recommender System Probabilistic Analysis Data Perturbation
Content Type	Text
Resource Type	Article

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