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دسته بندی:
سیستم های توصیه گر - recommender systems
سال انتشار:
2019
عنوان انگلیسی مقاله:
Towards a more reliable privacy-preserving recommender system
ترجمه فارسی عنوان مقاله:
به سمت یک سیستم توصیه گر برای حفظ حریم خصوصی قابل اطمینان تر
منبع:
Sciencedirect - Elsevier - Information Sciences, 482 (2019) 248-265: doi:10:1016/j:ins:2018:12:085
نویسنده:
Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin
چکیده انگلیسی:
This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and ex- istence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept pri- vate in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the pop- ular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy.© 2019 Elsevier Inc. All rights reserved.
Keywords: Privacy-preserving recommendation | Differential privacy | Secure distributed matrix factorization | Randomized response algorithms
قیمت: رایگان
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