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Improved collaborative filtering recommendation algorithm based on differential privacy protection

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Abstract

In order to receive efficient personalized recommendation, users have to provide personal information to service providers. However, in this process, personal private data are in an extremely dangerous situation. Personalized recommendation technology based on privacy protection can enable users to enjoy personalized recommendations, while private data are also protected. In this paper, an efficient privacy-preserving collaborative filtering algorithm is proposed, which is based on differential privacy protection and time factor. The proposed method used the MovieLens data set in the experiment. Experimental results showed that the proposed method can effectively protect the private data, but the accuracy of recommendation is slightly inferior than the traditional collaborative filtering algorithm.

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References

  1. Tang L, Jiang Y, Li L et al (2015) Personalized recommendation via parameter-free contextual bandits. In: International ACM SIGIR Conference on Research and Development in Information Retrieval. ACM, pp 323–332

  2. Blum A, Dwork C, McSherry F et al (2005) Practical privacy: the Su LQ framework. In: Proceedings of the Twenty-Fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems. ACM, pp 128–138

  3. Friedman A, Schuster A (2010) Data mining with differential privacy. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 493–502

  4. Mohammed N, Chen R (2011) Differentially private data release for data mining. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 493–501

  5. Moreno MN, García FJ, Polo MJ et al (2004) Using association analysis of web data in recommender systems. In: International Conference on E-Commerce and Web Technology, pp 11–20

  6. Sun X, Kong F, Chen H (2005) Using quantitative association rules in collaborative filtering. In: International Conference on Advances in Web-Age Information Management. Springer, pp 822–827

  7. Chun J, Oh JY, Kwon S et al (2004) Simulating the effectiveness of using association rules for recommendation systems. In: Asian Simulation Conference on Systems Modeling and Simulation: Theory and Applications. Springer, Berlin, pp 306–314

  8. Li J, Tang B, Cercone N (2004) Applying association rules for interesting recommendations using rule templates. In: Advances in Knowledge Discovery and Data Mining. Springer, Berlin, pp 166–170

  9. Yin C, Feng L, Ma L (2016) An improved Hoeffding-ID data-stream classification algorithm. J Supercomput 72(7):2670–2681

    Article  Google Scholar 

  10. Mooney RJ, Bennett PN, Roy L (2017) Book recommending using text categorization with extracted information. In: Recommender Systems Papers from Workshop 2017, pp 49–54

  11. Pazzani M, Billsus D (1997) Learning and revising user profiles: the identification of interesting web sites. Mach Learn 27(3):313–331

    Article  Google Scholar 

  12. Salem B, Rauterberg M (2004) Multiple user profile merging (MUPE): key challenges for environment awareness. In: European Symposium on Ambient Intelligence 2004, pp 196–206

  13. Sparacino F (2003) Sto(ry)chastics: a Bayesian network architecture for user modeling and computational storytelling for interactive spaces. In: UBICOMP 2003: Ubiquitous Computing. Springer, Berlin, pp 54–72

  14. McSherry F, Mironov I (2009) Differentially private recommender systems: building privacy into the net. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ACM, pp 627–636

  15. Machanavajjhala A, Korolova A, Sarma AD (2011) Personalized social recommendations: accurate or private. Proc VLDB Endow 4(7):440–450

    Article  Google Scholar 

  16. Shen Y, Jin H (2014) Privacy-preserving personalized recommendation: an instance-based approach via differential privacy. In: IEEE International Conference on Data Mining 2014, pp 540–549

  17. Jin H, Jin H (2016) EpicRec: towards practical differentially private framework for personalized recommendation. In: ACM Sigsac Conference on Computer and Communications Security. ACM, pp 180–191

  18. Huang J, Qi J, Xu Y, Chen J (2015) A privacy-enhancing model for location-based personalized recommendations. Distrib Parallel Databases 33(2):253–276

    Article  Google Scholar 

  19. Ahmed KW, Mouri IJ, Zaman R et al (2016) A privacy preserving personalized group recommendation framework. In: International Conference on Advanced Computing. IEEE, pp 594–598

  20. Dwork C (2008) Differential privacy: a survey of results. In: International Conference on Theory and Applications of MODELS of Computation. Springer, pp 1–19

  21. Clifton C, Tassa T (2013) On syntactic anonymity and differential privacy. In: 29th International Conference on. IEEE, pp 88–93

  22. Yin C, Xi J, Sun R, Wang J (2018) Location privacy protection based on differential privacy strategy for big data in industrial internet-of-things. IEEE Trans Ind Inf 14(8):3628–3636

    Article  Google Scholar 

  23. Kumari V, Chakravarthy S (2016) Cooperative privacy game: a novel strategy for preserving privacy in data publishing. Hum Centric Comput Inf Sci 6(1):12

    Article  Google Scholar 

  24. Boutet A, Frey D, Guerraoui R, Jégou A, Kermarrec AM (2016) Privacy-preserving distributed collaborative filtering. Computing 98(8):827–846

    Article  MathSciNet  Google Scholar 

  25. Chai T, Draxler RR (2014) Root mean square error (RMSE) or mean absolute error (MAE)?–arguments against avoiding RMSE in the literature. Geosci Model Dev 7(3):1247–1250

    Article  Google Scholar 

  26. Yin C, Zhang S (2017) Parallel implementing improved k-means applied for image retrieval and anomaly detection. Multimed Tools Appl 76(16):16911–16927

    Article  Google Scholar 

  27. Yin C, Xia L, Zhang S, Sun R, Wang J (2017) Improved clustering algorithm based on high-speed network data stream. Soft Comput 22(13):4185–4195

    Article  Google Scholar 

  28. Barzaiq OO, Loke SW (2016) Personal destination pattern analysis with applications to mobile advertising. Hum Centric Comput Inf Sci 6(1):17

    Article  Google Scholar 

  29. Keegan N, Ji SY, Chaudhary A, Concolato C, Yu B, Jeong DH (2016) A survey of cloud-based network intrusion detection analysis. Hum Centric Comput Inf Sci 6(1):19

    Article  Google Scholar 

  30. Yin C, Zhang S, Yin Z, Wang J (2017) Anomaly detection model based on data stream clustering. Cluster Comput 2017:1–10

    Google Scholar 

  31. Wang J, Cao J, Li B, Lee S, Sherratt S (2015) Bio-inspired ant colony optimization based clustering algorithm with mobile sinks for applications in consumer home automation networks. IEEE Trans Consum Electron 61(4):438–444

    Article  Google Scholar 

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Acknowledgements

This work was funded by the National Natural Science Foundation of China (61772282, 61772454, 61811530332). It was also supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD), Postgraduate Research & Practice Innovation Program of Jiangsu Province (KYCX18_1032), Chongqing Research Program of Basic Research and Frontier Technology (No. cstc2015jcyjA40026, No. cstc2016jcyjA0568), Natural Science Foundation of Jiangsu Province (BK20150460) and Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET). It was also funded by the open research fund of Key Lab of Broadband Wireless Communication and Sensor Network Technology (Nanjing University of Posts and Telecommunications), Ministry of Education. Professor Jin Wang is the corresponding author.

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Authors and Affiliations

  1. School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Nanjing University of Information Science and Technology, Nanjing, 210044, China

    Chunyong Yin, Lingfeng Shi & Ruxia Sun

  2. School of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha, 410004, China

    Jin Wang

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  1. Chunyong Yin
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  2. Lingfeng Shi
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  3. Ruxia Sun
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  4. Jin Wang
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Correspondence to Jin Wang.

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Yin, C., Shi, L., Sun, R. et al. Improved collaborative filtering recommendation algorithm based on differential privacy protection. J Supercomput 76, 5161–5174 (2020). https://doi.org/10.1007/s11227-019-02751-7

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  • DOI: https://doi.org/10.1007/s11227-019-02751-7

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