Earticle

초록

영어

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 order to protect her privacy. For the recommendation server, two server-side recommendation algorithms are proposed, which operate under the condition that only a generalized user context is given. The experimental results have shown that, using our approaches, the user context privacy can be achieved without a significant degradation of the recommendation accuracy.

목차

Abstract
 1. Introduction
 2. Background and Related Work
  2.1. Context-aware Recommender System
  2.2 Privacy in Context-aware System
 3. Problem Formulation
  3.1 System Model
  3.2. Design Goals
  3.3 Threat Model
 4. Solution
  4.1 Client-side Context Generalization
  4.2. Server-side Recommendation
 5. Experiments and Analysis
  5.1. Dataset
  5.2. Metrics
  5.3 Results
 6. Conclusion
 Acknowledgments
 References

키워드

저자

• Yonglei Yao [ School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science & Technology, Nanjing 210044, China ]
• Jingfa Liu [ School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing 210044, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605