Zheng, Xiaoyao and Luo, Yonglong and Zhang, Ji 
ORCID: https://orcid.org/0000-0001-7167-6970 and Sun, Liping and Chen, Fulong
(2018)
A novel differential privacy recommendation method based on distributed framework.
In: 2018 Workshop on Scalable and Applicable Recommendation Systems (SAREC 2018), in conjunction with the 18th IEEE International Conference on Data Mining (ICDM 2018), 17-20 Nov 2018, 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.
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| Item Type: | Conference or Workshop Item (Commonwealth Reporting Category E) (Paper) |
|---|---|
| Refereed: | Yes |
| Item Status: | Live Archive |
| Additional Information: | Files associated with this item cannot be displayed due to copyright restrictions. |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Faculty/School / Institute/Centre: | Historic - Faculty of Health, Engineering and Sciences - School of Agricultural, Computational and Environmental Sciences (1 Jul 2013 - 5 Sep 2019) |
| Date Deposited: | 25 May 2020 23:11 |
| Last Modified: | 03 Jun 2020 05:41 |
| Uncontrolled Keywords: | distributed framework, location-based service, order preserving encryption, recommender system |
| Fields of Research (2008): | 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080105 Expert Systems |
| Fields of Research (2020): | 46 INFORMATION AND COMPUTING SCIENCES > 4605 Data management and data science > 460510 Recommender systems |
| Identification Number or DOI: | https://doi.org/10.1109/ICDMW.2018.00189 |
| URI: | http://eprints.usq.edu.au/id/eprint/36148 |
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