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SCIENCE CHINA Information Sciences, Volume 60, Issue 9: 092101(2017) https://doi.org/10.1007/s11432-015-0981-4

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

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  • ReceivedMar 9, 2016
  • AcceptedApr 22, 2016
  • PublishedOct 13, 2016

Abstract

Location-aware recommender systems that use location-based ratings to produce recommendations have recently experienced a rapid development and draw significant attention from the research community. However, current work mainly focused on high-quality recommendations while underestimating privacy issues, which can lead to problems of privacy. Such problems are more prominent when service providers, who have limited computational and storage resources, leverage on cloud platforms to fit in with the tremendous number of service requirements and users. In this paper, we propose a novel framework, namely APPLET, for protecting user privacy information, including locations and recommendation results, within a cloud environment. Through this framework, all historical ratings are stored and calculated in ciphertext, allowing us to securely compute the similarities of venues through Paillier encryption, and predict the recommendation results based on Paillier, commutative, and comparable encryption. We also theoretically prove that user information is private and will not be leaked during a recommendation. Finally, empirical results over a real-world dataset demonstrate that our framework can efficiently recommend POIs with a high degree of accuracy in a privacy-preserving manner.


Funded by

National High Technology Research and Development Program(863 Program)

China 111 Project(B16037)

Fundamental Research Funds for the Central Universities(JB150309)

National Natural Science Foundation of China(61202179)

National Natural Science Foundation of China(U1135002)

National Natural Science Foundation of China(U1405255)

National Natural Science Foundation of China(U1509214)

National Natural Science Foundation of China(61502368)

"source" : null , "contract" : "2015AA016007"

Shaanxi Provincial Natural Science Foundation(2015JQ6227)

Fundamental Research Funds for the Central Universities(JB150308)


Acknowledgment

Acknowledgments

This work was supported by National Natural Science Foundation of China (Grant Nos. 61202179, U1405255, 61502368, U1509214, U1135002), National High Technology Research and Development Program (863 Program) (Grant No. 2015AA016007), Shaanxi Provincial Natural Science Foundation (Grant No. 2015JQ6227), China 111 Project (Grant No. B16037), and Fundamental Research Funds for the Central Universities (Grant Nos. JB150308, JB150309).


References

[1] Zheng Y, Capra L, Wolfson O, et al. Urban computing: concepts, methodologies, and applications. ACM Trans Intell Syst Tech, 2014, 5: 38 Google Scholar

[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 CrossRef Google Scholar

[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. Google Scholar

[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. Google Scholar

[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 Google Scholar

[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 CrossRef Google Scholar

[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. Google Scholar

[8] Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Advances in Cryptology --- EUROCRYPT. Berlin: Springer, 1999. 223--238. Google Scholar

[9] Furukawa J. Request-based comparable encryption. In: Computer Security --- ESORICS. Berlin: Springer, 2013. 129--146. Google Scholar

[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. Google Scholar

[11] Dai W. Commutative-like encryption: a new characterization of ElGamal. arXiv:1011.3718. Google Scholar

[12] ElGamal T. A public key cryptosystem and a signature scheme based on discrete logarithms. In: Advances in Cryptology. Berlin: Springer, 1984. 10--18. Google Scholar

[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. Google Scholar

[14] Furukawa J. Short comparable encryption. In: Cryptology and Network Security. Berlin: Springer, 2014. 337--352. Google Scholar

[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 Google Scholar

[16] Goldreich O. Foundations of Cryptography: Volume 2, Basic Applications. Cambridge: Cambridge University Press, 2009. Google Scholar

[17] Bost R, Popa R A, Tu S, et al. Machine learning classification over encrypted data. IACR Cryptology ePrint Archive, 2014, 331. Google Scholar

[18] Scott J. UMN/Sarwat foursquare dataset. https://archive.org/details/201309\_foursquare\_dataset\_umn. Google Scholar

[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. Google Scholar

[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. Google Scholar

[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 Google Scholar

[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. Google Scholar

[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 CrossRef Google Scholar

[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. Google Scholar

[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 CrossRef Google Scholar

[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. Google Scholar

[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. Google Scholar

[28] To H, Ghinita G, Shahabi C. A framework for protecting worker location privacy in spatial crowdsourcing. Proc VLDB Endowment, 2014, 7: 919-930 CrossRef Google Scholar

[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. Google Scholar

[30] Popa R A, Redfield C, Zeldovich N, et al. CryptDB: processing queries on an encrypted database. Commun ACM, 2012, 55: 103-111 Google Scholar

[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. Google Scholar

[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. Google Scholar

[33] Staff C. Recommendation algorithms, online privacy, and more. Commun ACM, 2009, 52: 10-11 Google Scholar

[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. Google Scholar

[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. Google Scholar

[36] Guerraoui R, Kermarrec A M, Patra R, et al. D2P: distance-based differential privacy in recommenders. Proc VLDB Endowment, 2015, 8: 862-873 CrossRef Google Scholar

[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. Google Scholar

[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. Google Scholar

[39] Hoens T R, Blanton M, Steele A, et al. Reliable medical recommendation systems with patient privacy. ACM Trans Intell Syst Tech, 2013, 4: 67-873 Google Scholar

[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 CrossRef Google Scholar

[41] Xin Y, Jaakkola T. Controlling privacy in recommender systems. In: Advances in Neural Information Processing Systems, Montreal, 2014. 3: 2618--2626. Google Scholar

[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 Google Scholar

[43] A{\"{\i}}meur 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 CrossRef Google Scholar

[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. Google Scholar

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