User Friendly Preferential Private Recommendation

doi:10.19682/j.cnki.1005-8885.2022.1006

The Journal of China Universities of Posts and Telecommunications ›› 2022, Vol. 29 ›› Issue (3): 43-53.doi: 10.19682/j.cnki.1005-8885.2022.1006

Previous Articles Next Articles

User Friendly Preferential Private Recommendation

1. School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2. China Unicom Network Technology Research Institute, Beijing 100044, China

Received:2021-04-06 Revised:2021-09-25 Online:2022-06-30 Published:2022-06-30
Contact: Yang Jingjing E-mail:19111044@bjtu.edu.cn
Supported by:
This work was supported by the National Natural Science Foundation of China (61872031).

Abstract

Abstract: To provide preferential protection for users while keeping good service utility, a preferential private recommendation framework ( named as PrefER) is proposed. In this framework, a preferential budget allocation scheme is designed and implemented at the system side to provide multilevel protection. And users' preference is utilized at the user side to improve recommendation performance without increasing users' burden. This framework is generic enough to be employed with other schemes. Recommendation accuracy based on the MovieLens dataset by the collaborative filtering schemes and PrefER are compared and analyzed. The experimental results show that PrefER can provide preferential privacy protection with the improvement of recommendation accuracy.

Key words: preferential privacy protection, differential privacy, top-k recommendation, personalized service

Yang Jingjing, Guo Yuchun, Feng Tingting, Chen Yishuai. User Friendly Preferential Private Recommendation[J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 43-53.

References

1. WEINSBERG U, BHAGAT S, IOANNIDIS S, et al. BlurMe: Inferring and obfuscating user gender based on ratings. Proceedings of the 6th ACM Conference on Recommender Systems (RecSys’12), 2012, Sept 9-13, Dublin, Ireland. New York, NY, USA: ACM, 2012: 195-202.

2. FENG T T, GUO Y C, CHEN Y S, et al. Tags and titles of videos you watched tell your gender. Proceedings of the 2014 IEEE International Conference on Communications (ICC’14), 2014, Jun 10-14, Sydney, Australia. Piscataway, NJ, USA: IEEE, 2014: 1837-1842.

3. GAZZETTA M R, LA L K, KARNAWAT M P. Secure management of document in a client-server environment. US11623014. 2008.

4. CALANDRINO J A, KILZER A, NARAYANAN A, et al. "You might also like:" Privacy risks of collaborative filtering. Proceedings of the 2011 IEEE Symposium on Security and Privacy, 2011, May 22-25, Oakland, CA, USA. Piscataway, NJ, USA: IEEE, 2011: 231-246.

5. FENG T T, GUO Y C, CHEN Y S. A differential private collaborative filtering framework based on privacy-relevance of topics. Proceedings of the 2016 IEEE Symposium on Computers and Communication (ISCC’16), 2016, Jun 27-30, Messina, Italy. Piscataway, NJ, USA: IEEE, 2016: 946-951.

6. JORGENSEN Z, YU T, CORMODE G. Conservative or liberal? Personalized differential privacy. Proceedings of the IEEE 31st International Conference on Data Engineering, 2015, Apr 13-17, Seoul, Republic of Korea. Piscataway, NJ, USA: IEEE, 2015: 1023-1034.

7. FREDRIKSON M, JHA S, RISTENPART T. Model inversion attacks that exploit confidence information and basic countermeasures. Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS’15), 2015, Oct 12-16, Denver, CO, USA. New York, NY, USA: ACM, 2015: 1322-1333.

8. KALELI C, POLAT H. P2P collaborative filtering with privacy. Turkish Journal of Electrical Engineering & Computer Sciences, 2010, 18(1): 101-116.

9. BERKOVSKY S, EYTANI Y, KUFLIK T, et al. Enhancing privacy and preserving accuracy of a distributed collaborative filtering. Proceedings of the 2007 ACM Conference on Recommender Systems (RecSys’07), 2007, Oct 19-20, Minneapolis, MN, USA. New York, NY, USA: ACM, 2007: 9-16.

10. PARAMESWARAN R, BLOUGH D M. Privacy preserving collaborative filtering using data obfuscation. Proceedings of the 2007 IEEE International Conference on Granular Computing (GRC’07), 2007, Nov 2-4, Fremont, CA, USA. Piscataway, NJ, USA: IEEE, 2007: 380-380.

11. FENG T T, GUO Y C, CHEN Y S. Can user privacy and recommendation performance be preserved simultaneously? Computer Communications, 2015, 68: 17-24.

12. DWORK C. Differential privacy: A survey of results. Theory and Applications of Models of Computation: Proceedings of the 5th International Conference on Theory and Applications of Models of Computation (TAMC’08), 2008, Apr 25-29, Xi'an, China. LNTCS 4978. Berlin, Germany: Springer, 2008: 1-19.

13. ZHU X, SUN Y Q. Differential privacy for collaborative filtering recommender algorithm. Proceedings of the 2016 ACM on International Workshop on Security and Privacy Analytics (IWSPA’16), 2016, Mar 11, New Orleans, LA, USA. New York, NY, USA: ACM, 2016: 9-16.

14. MCSHERRY F, MIRONOV I. Differentially private recommender systems: Building privacy into the netflix prize contenders. Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), 2009, Jun 28-Jul 1, Paris, France. New York, NY, USA: ACM, 2009: 627-636.

15. ZHU T Q, REN Y L, ZHOU W L, et al. An effective privacy preserving algorithm for neighborhood-based collaborative filtering. Future Generation Computer Systems, 2014, 36: 142-155.

16. LIU X, LIU A, ZHANG X L, et al. When differential privacy meets randomized perturbation: A hybrid approach for privacy-preserving recommender system. Database Systems for Advanced Applications: Proceedings of the 22nd International Conference on Database Systems for Advanced Applications (DASFAA’17), 2017, Mar 27-30, Suzhou, China. LNISA 10177. Berlin, Germany: Springer, 2017: 576-591.

17. YANG J, LI X Y, SUN Z L, et al. A differential privacy framework for collaborative filtering. Mathematical Problems in Engineering, 2019, Article 1460234/1-11.

18. HU M, WU D, WU R, et al. RAP: A light-weight privacy-preserving framework for recommender systems. IEEE Transactions on Services Computing, 2021, Early Access Article, DOI: 10.1109/TSC.2021.3065035.

19. LI G S, YIN G S, YANG J S, et al. SDRM-LDP: A recommendation model based on local differential privacy. Wireless Communications and Mobile Computing, 2021, Article 6640667/1-15

20. CHI X X, YAN C, WANG H, et al. Amplified locality-sensitive hashing-based recommender systems with privacy protection. Concurrency and Computation: Practice and Experience, 2020: e5681.

21. MENG X Y, WANG S H, SHU K, et al. Personalized privacy-preserving social recommendation. Proceedings of the 32nd AAAI Conference on Artificial Intelligence and 30th Innovative Applications of Artificial Intelligence Conference and 8th AAAI Symposium on Educational Advances in Artificial Intelligence (AAAI'18/IAAI'18/EAAI'18), 2018, Feb 2-7, New Orleans, LA, USA. Menlo Park, CA, USA: American Association for Artificial Intelligence (AAAI), 2018: 3796-3803.

22. SHIN H, KIM S, SHIN J, et al. Privacy enhanced matrix factorization for recommendation with local differential privacy. IEEE Transactions on Knowledge and Data Engineering, 2018, 30(9): 1770-1782.

23. NGUYÊN T T, XIAO X K, YANG Y, et al. Collecting and analyzing data from smart device users with local differential privacy. arXiv Preprint, arXiv:1606.05053, 2016.

24. WARNER S L. Randomized response: A survey technique for eliminating evasive answer bias. Journal of the American Statistical Association, 1965, 60(309): 63-69.

25. DWORK C, MCSHERRY F, NISSIM K, et al. Calibrating noise to sensitivity in private data analysis. Theory of Cryptography: Proceedings of the 3rd Theory of Cryptography Conference (TCC’06), 2006, Mar 4-7, New York, NY, USA. LNSC 3876. Berlin, Germany: Springer, 2006: 265-284.

26. NISSIM K, RASKHODNIKOVA S, SMITH A. Smooth sensitivity and sampling in private data analysis. Proceedings of the 39th annual ACM Symposium on Theory of Computing (STOC’07), 2007, Jun 11-13, San Diego, CA, USA. New York, NY, USA: ACM, 2007: 75-84

27. MCSHERRY F, TALWAR K. Mechanism design via differential privacy. Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS'07), 2007, Oct 21-23, Providence, RI, USA. Piscataway, NJ, USA: IEEE, 2007: 94-103

Metrics

Comments

Copyright © 2020 The Journal of China Universities of Posts and Telecommunications
　 Adress: P.O. Box 231,Beijing University of Posts and Telecommunications,10 Xi Tucheng Road,Beijing 100876,P.R.China　Post Code: 100081
Tel：86-010-62282493　Fax： 86-010-62283461　E-mail: jchupt@bupt.edu.cn
Support by: Beijing Magtech Co.Ltd

[1]	Zhang Guangwei, Zhao Bing, Li Ruifan. Multi-level fusion with deep neural networks for multimodal sentiment classification [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 25-33.
[2]	Li Qinghua, Wang Jiahui, Li Haiming, Feng Chao. HQD-RRT*: a high-quality path planner for mobile robot in dynamic environment [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 69-80.
[3]	Miao Jiansong, Li Hairui, Zheng Ziyuan, Wang Chu, Zhao Zhenmin. Secrecy Energy Efficiency Maximization for UAV-Enabled Multi-Hop Mobile Relay System [J]. The Journal of China Universities of Posts and Telecommunications, 2022, 29(3): 81-91.
[4]	Tang Fei, Dong Kun, Ye Zhangtao, Ling Guowei. Authentication scheme for industrial Internet of things based on DAG blockchain [J]. The Journal of China Universities of Posts and Telecommunications, 2021, 28(6): 1-12.

User Friendly Preferential Private Recommendation

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 4

Recommended Articles

Metrics

Comments