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A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection

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Abstract

With the popularity of social networks such as Facebook and Twitter, more information such as individual’s social connections is considered to make personalized multimedia recommendation, compared to traditional approaches based on the rating matrix. However, the massive data information used for recommendation often contains much personal privacy information. Once the information is obtained by attackers, user’s privacy will be revealed directly or indirectly. This paper proposes a privacy preserving method based on weighted noise injection technique to address the issue of multimedia recommendation in the context of social networks. More specifically, first, we extract core users from entire users. The extracted core users can represent the features of all users adequately. Only the relevant data of core users are then used for rating prediction. Second, we inject different noises to the rating matrix of core users according to different relations between the target user and core users. Third, we use the perturbed matrix to predict the ratings of unused multimedia resources for the target user based on a mixed collaborative filtering approach. By comparing with the traditional noise injection method, the experimental results show that the proposed approach can get better performance of privacy preserving multimedia recommendation.

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References

  1. 1.

    Adomavicius G, Tuzhilin A (2005) Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans Knowl Data Eng 17(6):734–749

  2. 2.

    Banerjee S, Hegde N, Massoulié L (2012) The price of privacy in untrusted recommendation engines. In Communication, control, and computing (Allerton), 2012 50th annual Allerton conference on (pp 920–927). IEEE

  3. 3.

    Berkovsky S, Eytani Y, Kuflik T, Ricci F (2007) Enhancing privacy and preserving accuracy of a distributed collaborative filtering. InProceedings of the 2007 ACM conference on recommender systems (pp 9–16). ACM

  4. 4.

    Calandrino JA, Kilzer A, Narayanan A, Felten EW, Shmatikov V (2011) "You might also like:" Privacy risks of collaborative filtering. In 2011 I.E. symposium on security and privacy (pp 231–246). IEEE

  5. 5.

    Canny J (2002) Collaborative filtering with privacy via factor analysis. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (Vol. 18, pp. 238–245). ACM

  6. 6.

    Chen P, Xie H, Maslov S, Redner S (2007) Finding scientific gems with Google’s PageRank algorithm. J Informet 1(1):8–15

  7. 7.

    Chen J, Song X, Nie L, Wang X, Zhang H, Chua TS (2016) Micro tells macro: predicting the popularity of micro-videos via a transductive model. In Proceedings of the 2016 ACM on multimedia conference (pp 898–907). ACM

  8. 8.

    Das AS, Datar M, Garg A, Rajaram S (2007) Google news personalization: scalable online collaborative filtering. In Proceedings of the 16th international conference on World Wide Web (pp 271–280). ACM

  9. 9.

    Dwork C (2008) Differential privacy: a survey of results. InInternational conference on theory and applications of models of computation (pp. 1–19). Springer, Berlin Heidelberg

  10. 10.

    Fagin R, Kolaitis PG, Miller RJ, Popa L (2003) Data exchange: semantics and query answering. Theor Comput Sci 336(1):89–124

  11. 11.

    Fouss F, Pirotte A, Renders JM, Saerens M (2007) Random-walk computation of similarities between nodes of a graph with application to collaborative recommendation. IEEE Trans Knowl Data Eng 19(3):355–369

  12. 12.

    Iacobucci, D. (2005). Six degrees: the science of a connected age by duncan j. watts. Reflections,n:61(1), 93

  13. 13.

    Kim BM, Li Q (2004). Probabilistic model estimation for collaborative filtering based on items attributes. In Proceedings of the 2004 IEEE/WIC/ACM international conference on web intelligence (pp 185–191). IEEE Computer Society

  14. 14.

    Li T, Li N, Zhang J (2009) Modeling and integrating background knowledge in data anonymization. In 2009 I.E. 25th international conference on data engineering (pp 6–17). IEEE

  15. 15.

    Lin H, Yang X, Wang W (2014) A content-boosted collaborative filtering algorithm for personalized training in interpretation of radiological imaging. J Digit Imaging 27(4):449–456

  16. 16.

    Linden G, Smith B, York J (2003) Amazon. Com recommendations: item-to-item collaborative filtering. IEEE Internet Comput 7(1):76–80

  17. 17.

    Liu X, He Q, Tian Y, Lee WC, McPherson J, Han J (2012a) Event-based social networks: linking the online and offline social worlds. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 1032–1040). ACM

  18. 18.

    Liu X, He Q, Tian Y, Lee WC, McPherson J, Han J (2012b) Event-based social networks: linking the online and offline social worlds. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 1032–1040). ACM

  19. 19.

    Liu X, Xia Y, Jiang S, Xia F, Wang Y (2013a) Hierarchical attribute-based access control with authentication for outsourced data in cloud computing. In 2013 12th IEEE international conference on trust, security and privacy in computing and communications (pp 477–484). IEEE

  20. 20.

    Liu X, Song M, Tao D, Liu Z, Zhang L, Chen C, Bu J (2013b) Semi-supervised node splitting for random forest construction. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp 492–499)

  21. 21.

    Liu X, Xia Y, Chen W, Xiang Y, Hassan MM, Alelaiwi A (2016) SEMD: secure and efficient message dissemination with policy enforcement in VANET. J Comput Syst Sci

  22. 22.

    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(pp 627–636). ACM

  23. 23.

    Narayanan P (2004) U.S. Patent Application No. 10/709, 161

  24. 24.

    Narayanan A, Shmatikov V (2009) De-anonymizing social networks. In 2009 30th IEEE symposium on security and privacy (pp 173–187). IEEE

  25. 25.

    Parameswaran R, Blough DM (2007) Privacy preserving collaborative filtering using data obfuscation. In Granular Computing, 2007. GRC 2007. IEEE international conference on (pp 380–380). IEEE

  26. 26.

    Park ST, Pennock DM (2007) Applying collaborative filtering techniques to movie search for better ranking and browsing. In Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 550–559). ACM

  27. 27.

    Polat H, Du W (2003) Privacy-preserving collaborative filtering using randomized perturbation techniques

  28. 28.

    Salakhutdinov R, Mnih A (2008) Bayesian probabilistic matrix factorization using Markov chain Monte Carlo. In Proceedings of the 25th international conference on Machine learning (pp 880–887). ACM

  29. 29.

    Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th international conference on World Wide Web (pp 285–295). ACM

  30. 30.

    Schafer JB, Frankowski D, Herlocker J, Sen S (2007) Collaborative filtering recommender systems. In: The adaptive web. Berlin Heidelberg, Springer, pp 291–324

  31. 31.

    Schein AI, Popescul A, Ungar LH, Pennock DM (2002) Methods and metrics for cold-start recommendations. In Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval (pp 253–260). ACM

  32. 32.

    Song X, Nie L, Zhang L, Akbari M, Chua TS (2015a) Multiple social network learning and its application in volunteerism tendency prediction. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval (pp 213–222). ACM

  33. 33.

    Song X, Nie L, Zhang L, Liu M, Chua TS (2015b) Interest inference via structure-constrained multi-source multi-task learning. InProceedings of the International Joint Conference on Artificial Intelligence(pp 2371–2377)

  34. 34.

    Statistical Report on Internet Development in China 2016. http://www.cnnic.cn/gywm/xwzx/rdxw/2016/201608/W020160803204144417902.pdf

  35. 35.

    Tuzhilin A, Koren Y, Bennett J, Elkan C, Lemire D (2008) Large-Scale Recommender Systems and the Netflix Prize Competition. In KDD Proceedings

  36. 36.

    ​Xia Y, Ren X, Peng Z, Zhang J, She L (2016) Effectively identifying the influential spreaders in large-scale social networks. Multimed Tools Appl 75(15):8829–8841

  37. 37.

    Xia Y, Liu X, Xia F, Wang G (2016) A reduction of security notions in designated confirmer signatures. Theor Comput Sci 618:1–20

  38. 38.

    Yin H, Sun Y, Cui B, Hu Z, Chen L (2013) LCARS: a location-content-aware recommender system. In Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 221–229). ACM

  39. 39.

    Zhan J, Hsieh CL, Wang IC, Hsu TS, Liau CJ, Wang DW (2010) Privacy-preserving collaborative recommender systems. IEEE Trans Syst Man Cybern Part C Appl Rev 40(4):472–476

  40. 40.

    Zhang W, Wang J, Feng W (2013a) Combining latent factor model with location features for event-based group recommendation. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 910–918). ACM

  41. 41.

    Zhang W, Wang J, Feng W (2013b) Combining latent factor model with location features for event-based group recommendation. InProceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining (pp 910–918). ACM

  42. 42.

    Zhang L, Gao Y, Xia Y, Lu K, Shen J, Ji R (2014a) Representative discovery of structure cues for weakly-supervised image segmentation. IEEE Trans Multimed 16(2):470–479

  43. 43.

    Zhang L, Yang Y, Gao Y, Yu Y, Wang C, Li X (2014b) A probabilistic associative model for segmenting weakly supervised images. IEEE Trans Image Process 23(9):4150–4159

  44. 44.

    Zhang L, Gao Y, Xia Y, Dai Q, Li X (2015a) A fine-grained image categorization system by cellet-encoded spatial pyramid modeling. IEEE Trans Ind Electron 62(1):564–571

  45. 45.

    Zhang L, Xia Y, Ji R, Li X (2015b) Spatial-aware object-level saliency prediction by learning graphlet hierarchies. IEEE Trans Ind Electron 62(2):1301–1308

  46. 46.

    Zhang L, Li X, Nie L, Yan Y, Zimmermann R (2016a) Semantic photo Retargeting under noisy image labels. ACM Trans Multimed Comp Comm Appl (TOMM) 12(3):37

  47. 47.

    Zhang L, Yang Y, Wang M, Hong R, Nie L, Li X (2016b) Detecting densely distributed graph patterns for fine-grained image categorization. IEEE Trans Image Process 25(2):553–565

  48. 48.

    Zhou D, Zhang C, Liu L, Wang J, Zhou X (2004) Taxonomy-driven Computation of Product Recommendations. Thirteenth Acm International Conference on Information & Knowledge Management(Vol. 5, pp 406–415)

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Acknowledgements

The research is supported by “Natural Science Foundation of Hunan Province” (No.2016JJ3154), “National Natural Science Foundation of China” (No.61202095), “Scientific Research Project for Professors in Central South University, China” (No. 904010001), and “Innovation Project for Graduate Students in Central South University” (No. 502210017).

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Correspondence to Li Kuang.

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Dou, K., Guo, B. & Kuang, L. A privacy-preserving multimedia recommendation in the context of social network based on weighted noise injection. Multimed Tools Appl 78, 26907–26926 (2019). https://doi.org/10.1007/s11042-017-4352-3

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Keywords

  • Social network
  • Privacy preserving
  • Multimedia recommendation
  • Noise injection