APRS: a privacy-preserving location-aware recommender system based on differentially private histogram
نویسندگان
چکیده
منابع مشابه
On Privacy-preserving Context-aware Recommender System
Privacy is an important issue in Context-aware recommender systems (CARSs). In this paper, we propose a privacy-preserving CARS in which a user can limit the contextual information submitted to the server without sacrificing a significant recommendation accuracy. Specifically, for users, we introduce a client-side algorithm that the user can employ to generalize its context to some extent, in o...
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1389-1286/$ see front matter 2012 Elsevier B.V http://dx.doi.org/10.1016/j.comnet.2012.03.022 ⇑ Corresponding author. E-mail addresses: [email protected] (A. Pingle (W. Yu), [email protected] (N. Zhang), xinwenfu [email protected] (W. Zhao). We address issues related to privacy protection in location-based services (LBSs). Most existing privacy-preserving LBS techniques either require a trusted thi...
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With the wide adoption of handheld devices (e.g., smartphones, tablets), a large number of location-based services (also called LBSs) have flourished providing mobile users with real-time and contextual information on the move. Accounting for the amount of location information they are given by users, these services are able to track users wherever they go and to learn sensitive information abo...
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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 res...
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Nowadays, recommender systems have become an indispensable part of our daily life and provide personalized services for almost everything. However, nothing is for free – such systems have also upset the society with severe privacy concerns because they accumulate a lot of personal information in order to provide recommendations. In this work, we construct privacy-preserving recommendation proto...
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ژورنال
عنوان ژورنال: Science China Information Sciences
سال: 2017
ISSN: 1674-733X,1869-1919
DOI: 10.1007/s11432-017-9222-7