APPLET: a privacy-preserving framework for location-aware recommender system

Science China Information Sciences

Volumn 60, Issue 9, 2017, Pages

  Ma, Xindi ^a   Li, Hui ^a   Ma, Jianfeng ^a   Jiang, Qi ^a   Gao, Sheng ^b   Xi, Ning ^a   Lu, Di ^a  

^a XIDIAN UNIVERSITY   (China)

^b CENTRAL UNIVERSITY OF FINANCE AND ECONOMICS   (China)

Author keywords

collaborative filtering; homomorphic encryption; location based service; privacy preserving framework; recommender system

Indexed keywords

COLLABORATIVE FILTERING; CRYPTOGRAPHY; DATA PRIVACY; LOCATION; LOCATION BASED SERVICES; TELECOMMUNICATION SERVICES;

CLOUD ENVIRONMENTS; HIGH DEGREE OF ACCURACY; HO-MOMORPHIC ENCRYPTIONS; NUMBER OF SERVICES; PRIVACY PRESERVING; RESEARCH COMMUNITIES; SERVICE PROVIDER; STORAGE RESOURCES;

RECOMMENDER SYSTEMS;

EID: 84991628197     PISSN: 1674733X     EISSN: None     Source Type: Journal    
DOI: 10.1007/s11432-015-0981-4     Document Type: Article

References (44)

1. Zheng Y, Capra L, Wolfson O, et al. Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Tech, 2014, 5: 38
2. Sarwat M, Levandoski J J, Eldawy A, et al. LARS*: an efficient and scalable location-aware recommender system. IEEE Trans Knowl Data Eng, 2014, 26: 1384–1399
3. Brodkin J. Netflix shuts down its last data center, but it still runs a big it operation. http://arstechnica.com/information-technology/2015/08/netflix-shuts-down-its-last-data-center-but-still-runs-a-big-it-operation. 2015
4. Levi A, Mokryn O, Diot C, et al. Finding a needle in a haystack of reviews: cold start context-based hotel recommender system. In: Proceedings of the 6th ACM Conference on Recommender Systems, Dublin, 2012. 115–122
5. Celdran A H, Perez M G, Garcia C F, et al. PRECISE: privacy-aware recommender based on context information for cloud service environments. IEEE Commun Mag, 2014, 52: 90–96
6. Huang J, Qi J Z, Xu Y B, et al. A privacy-enhancing model for location-based personalized recommendations. Distrib Parallel Dat, 2015, 33: 253–276
7. Scipioni M P. Towards privacy-aware location-based recommender systems. In: Proceedings of the 7th International Federation for Information Processing Summer School, Trento, 2011. 1–8
8. Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Advances in Cryptology — EUROCRYPT. Berlin: Springer, 1999. 223–238
9. Furukawa J. Request-based comparable encryption. In: Computer Security — ESORICS. Berlin: Springer, 2013. 129–146
10. Sarwar B, Karypis G, Konstan J, et al. Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 2001. 285–295
11. Dai W. Commutative-like encryption: a new characterization of ElGamal. arXiv:1011.3718
12. ElGamal T. A public key cryptosystem and a signature scheme based on discrete logarithms. In: Advances in Cryptology. Berlin: Springer, 1984. 10–18
13. Weis S A. New foundations for efficient authentication, commutative cryptography, and private disjointness testing. Dissertation for Ph.D. Degree. Cambridge: Massachusetts Institute of Technology, 2006
14. Furukawa J. Short comparable encryption. In: Cryptology and Network Security. Berlin: Springer, 2014. 337–352
15. Lu R X, Zhu H, Liu X M, et al. Toward efficient and privacy-preserving computing in big data era. IEEE Netw, 2014, 28: 46–50
16. Goldreich O. Foundations of Cryptography: Volume 2, Basic Applications. Cambridge: Cambridge University Press, 2009
17. Bost R, Popa R A, Tu S, et al. Machine learning classification over encrypted data. IACR Cryptology ePrint Archive, 2014, 331
18. Scott J. UMN/Sarwat foursquare dataset. https://archive.org/details/201309 foursquare dataset umn
19. Ye M, Yin P F, Lee W C. Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, San Jose, 2010. 458–461
20. Liu B S, Hengartner U. pTwitterRec: a privacy-preserving personalized tweet recommendation framework. In: Proceedings of the 9th ACM Symposium on Information, Computer and Communications Security, Kyoto, 2014. 365–376
21. Samanthula B K, Cen L, Jiang W, et al. Privacy-preserving and efficient friend re-commendation in online social networks. Trans Data Privacy, 2015, 8: 141–171
22. Gao H J, Tang J L, Hu X, et al. Content-aware point of interest recommendation on location-based social networks. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence, Austin, 2015. 1721–1727
23. Gao S, Ma J F, Shi W S, et al. TrPF: a trajectory privacy-preserving framework for participatory sensing. IEEE Trans Inf Forensic Secur, 2013, 8: 874–887
24. Niu B, Li Q H, Zhu X Y, et al. Enhancing privacy through caching in location-based services. In: Proceedings of IEEE Conference on Computer Communications (INFOCOM), Kowloon, 2015. 1017–1025
25. Cicek A E, Nergiz M E, Saygin Y. Ensuring location diversity in privacy-preserving spatio-temporal data publishing. VLDB J, 2014, 23: 609–625
26. Andrés M E, Bordenabe N E, Chatzikokolakis K, et al. Geo-indistinguishability: differential privacy for location-based systems. In: Proceedings of the 20th ACM SIGSAC Conference on Computer & Communications Security. Berlin: Springer, 2013. 901–914
27. Xiao Y H, Xiong L. Protecting locations with differential privacy under temporal correlations. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, Denver, 2015. 1298–1309
28. To H, Ghinita G, Shahabi C. A framework for protecting worker location privacy in spatial crowdsourcing. Proc VLDB Endowment, 2014, 7: 919–930
29. Shao J, Lu R X, Lin X D. FINE: a fine-grained privacy-preserving location-based service framework for mobile devices. In: Proceedings of IEEE Conference on Computer Communications (INFOCOM), Toronto, 2014. 244–252
30. Popa R A, Redfield C, Zeldovich N, et al. CryptDB: processing queries on an encrypted database. Commun ACM, 2012, 55: 103–111
31. Calandrino J A, Kilzer A, Narayanan A, et al. “You might also like:” privacy risks of collaborative filtering. In: Proceedings of IEEE Symposium on Security and Privacy (S&P), California, 2011. 231–246
32. Bhagat S, Weinsberg U, Ioannidis S, et al. Recommending with an agenda: active learning of private attributes using matrix factorization. In: Proceedings of the 8th ACM Conference on Recommender Systems. New York: ACM, 2014. 65–72
33. Staff C. Recommendation algorithms, online privacy, and more. Commun ACM, 2009, 52: 10–11
34. Zhu J M, He P J, Zheng Z B, et al. A privacy-preserving QoS prediction framework for web service recommendation. In: Proceedings of IEEE International Conference on Web Services, New York, 2015. 241–248
35. Jorgensen Z, Yu T. A privacy-preserving framework for personalized, social recommendations. In: Proceedings of the 17th International Conference on Extending Database Technology, Athens, 2014. 571–582
36. Guerraoui R, Kermarrec A M, Patra R, et al. D2P: distance-based differential privacy in recommenders. Proc VLDB Endowment, 2015, 8: 862–873
37. Shen Y L, Jin H X. Privacy-preserving personalized recommendation: an instance-based approach via differential privacy. In: Proceedings of IEEE International Conference on Data Mining, Shenzhen, 2014. 540–549
38. Gong Y M, Guo Y X, Fang Y G. A privacy-preserving task recommendation framework for mobile crowdsourcing. In: Proceedings of IEEE Global Communications Conference, Austin, 2014. 588–593
39. Hoens T R, Blanton M, Steele A, et al. Reliable medical recommendation systems with patient privacy. ACM Trans Intell Syst Tech, 2013, 4: 67
40. Guo L, Zhang C, Fang Y G. A trust-based privacy-preserving friend recommendation scheme for online social networks. IEEE Trans Depend Secure Comput, 2015, 12: 413–427
41. Xin Y, Jaakkola T. Controlling privacy in recommender systems. In: Advances in Neural Information Processing Systems, Montreal, 2014. 3: 2618–2626
42. Ma T H, Zhou J J, Tang M L, et al. Social network and tag sources based augmenting collaborative recommender system. IEICE Trans Inf Syst, 2015, 98: 902–910
43. Aïmeur E, Brassard G, Fernandez J M, et al. Alambic: a privacy-preserving recommender system for electronic commerce. Int J Inf Secur, 2008, 7: 307–334
44. Zhu H S, Xiong H, Ge Y, et al. Mobile app recommendations with security and privacy awareness. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, 2014. 951–960