A novel differential privacy recommendation method based on distributed framework
Paper
Paper/Presentation Title | A novel differential privacy recommendation method based on distributed framework |
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Presentation Type | Paper |
Authors | Zheng, Xiaoyao (Author), Luo, Yonglong (Author), Zhang, Ji (Author), Sun, Liping (Author) and Chen, Fulong (Author) |
Editors | Tong, Hanghang, Li, Zhenhui (Jessie), Zhu, Feida and Yu, Jeffrey |
Journal or Proceedings Title | Proceedings of the 18th IEEE International Conference on Data Mining (ICDMW 2018) |
Article Number | 8637500 |
Number of Pages | 8 |
Year | 2018 |
Place of Publication | Los Alamitos, California |
ISBN | 9781538692882 |
Digital Object Identifier (DOI) | https://doi.org/10.1109/ICDMW.2018.00189 |
Web Address (URL) of Paper | https://ieeexplore.ieee.org/document/8637500 |
Conference/Event | 2018 Workshop on Scalable and Applicable Recommendation Systems (SAREC 2018), in conjunction with the 18th IEEE International Conference on Data Mining (ICDM 2018) |
Event Details | 2018 Workshop on Scalable and Applicable Recommendation Systems (SAREC 2018), in conjunction with the 18th IEEE International Conference on Data Mining (ICDM 2018) Event Date 17 to end of 20 Nov 2018 Event Location Singapore |
Abstract | With the rapid development of mobile Internet technology, the traditional recommender systems have not been well adapted to location-based recommendation services, and they also face the risk of privacy leaks. In this paper, a distributed privacy-preserving recommendation framework is proposed, and a singular value decomposition recommendation algorithm based on distributed framework is designed by using the differential privacy technique. Furthermore, we use an order-preserving encryption function to protect the locations of users' requests. Theoretical analysis and experimental evaluation on two real datasets show that the proposed method not only provides a stronger privacy protection, but also delivers a better recommendation performance than traditional recommendation algorithms. |
Keywords | distributed framework, location-based service, order preserving encryption, recommender system |
ANZSRC Field of Research 2020 | 460510. Recommender systems |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Anhui Normal University, China |
School of Agricultural, Computational and Environmental Sciences | |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q52w5/a-novel-differential-privacy-recommendation-method-based-on-distributed-framework
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