Advertisement

Springer Nature is making SARS-CoV-2 and COVID-19 research free. View research | View latest news | Sign up for updates

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

一种面向位置感知推荐系统的隐私保框架

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.

创新点

作为提供个性化位置服务的一种重要手段, 高速、高效的位置感知推荐服务成为当前研究的热点。然而, 涉及多方参与的传统推荐流程存在着用户私密信息复制、盗取等安全威胁, 给用户的隐私保护带来了新的挑战, 尤其是当服务提供者将数据外包给第三方云平台时, 隐私泄露问题会更加凸显。为解决上述问题, 本文提出了一种面向位置感知推荐系统的隐私保护框架, 通过利用Paillier加密、可交换加密和可比较加密实现位置服务的安全推荐。通过理论证明和分析, 在该框架下, 用户的位置隐私信息在推荐过程中得到了有效保护。最后, 本文设计实现该框架并通过真实数据集进行测试, 测试结果表明该框架能够准确高效的为用户返回推荐结果。

This is a preview of subscription content, log in to check access.

References

  1. 1

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

  2. 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. 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. 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. 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. 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. 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. 8

    Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Advances in Cryptology — EUROCRYPT. Berlin: Springer, 1999. 223–238

  9. 9

    Furukawa J. Request-based comparable encryption. In: Computer Security — ESORICS. Berlin: Springer, 2013. 129–146

  10. 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. 11

    Dai W. Commutative-like encryption: a new characterization of ElGamal. arXiv:1011.3718

  12. 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. 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. 14

    Furukawa J. Short comparable encryption. In: Cryptology and Network Security. Berlin: Springer, 2014. 337–352

  15. 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. 16

    Goldreich O. Foundations of Cryptography: Volume 2, Basic Applications. Cambridge: Cambridge University Press, 2009

  17. 17

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

  18. 18

    Scott J. UMN/Sarwat foursquare dataset. https://archive.org/details/201309 foursquare dataset umn

  19. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 30

    Popa R A, Redfield C, Zeldovich N, et al. CryptDB: processing queries on an encrypted database. Commun ACM, 2012, 55: 103–111

  31. 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. 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. 33

    Staff C. Recommendation algorithms, online privacy, and more. Commun ACM, 2009, 52: 10–11

  34. 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. 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. 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. 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. 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. 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. 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. 41

    Xin Y, Jaakkola T. Controlling privacy in recommender systems. In: Advances in Neural Information Processing Systems, Montreal, 2014. 3: 2618–2626

  42. 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. 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. 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

Download references

Author information

Correspondence to Ning Xi.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ma, X., Li, H., Ma, J. et al. APPLET: a privacy-preserving framework for location-aware recommender system. Sci. China Inf. Sci. 60, 092101 (2017). https://doi.org/10.1007/s11432-015-0981-4

Download citation

Keywords

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

关键词

  • 推荐系统
  • 基于位置的服务
  • 同态加密
  • 隐私保护
  • 协同过滤

--

  • 092101