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Engineering    2018, Vol. 4 Issue (1) : 21 -28
Research |
Toward Privacy-Preserving Personalized Recommendation Services
Cong Wang1,2(),Yifeng Zheng1,2,Jinghua Jiang1,3,Kui Ren4
1. Department of Computer Science, City University of Hong Kong, Hong Kong, China
2. City University of Hong Kong, Shenzhen Research Institute, Shenzhen, Guangdong 518057, China
3. Department of Computer Science and Technology, Xi’an Jiaotong University, Xi’an 710049, China
4. Institute of Cyber Security Research, Zhejiang University, Hangzhou, Zhejiang 310058, China

Recommendation systems are crucially important for the delivery of personalized services to users. With personalized recommendation services, users can enjoy a variety of targeted recommendations such as movies, books, ads, restaurants, and more. In addition, personalized recommendation services have become extremely effective revenue drivers for online business. Despite the great benefits, deploying personalized recommendation services typically requires the collection of users’ personal data for processing and analytics, which undesirably makes users susceptible to serious privacy violation issues. Therefore, it is of paramount importance to develop practical privacy-preserving techniques to maintain the intelligence of personalized recommendation services while respecting user privacy. In this paper, we provide a comprehensive survey of the literature related to personalized recommendation services with privacy protection. We present the general architecture of personalized recommendation systems, the privacy issues therein, and existing works that focus on privacy-preserving personalized recommendation services. We classify the existing works according to their underlying techniques for personalized recommendation and privacy protection, and thoroughly discuss and compare their merits and demerits, especially in terms of privacy and recommendation accuracy. We also identity some future research directions.

Keywords Privacy protection      Personalized recommendation services      Targeted delivery      Collaborative filtering      Machine learning     
Corresponding Authors: Cong Wang   
Just Accepted Date: 06 March 2018   Issue Date: 10 April 2018
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Cong Wang
Yifeng Zheng
Jinghua Jiang
Kui Ren
Cite this article:   
Cong Wang,Yifeng Zheng,Jinghua Jiang, et al. Toward Privacy-Preserving Personalized Recommendation Services[J]. Engineering, 2018, 4(1): 21 -28 .
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[1] Eric P. Xing,Qirong Ho,Pengtao Xie,Dai Wei. Strategies and Principles of Distributed Machine Learning on Big Data[J]. Engineering, 2016, 2(2): 179 -195 .
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