Advertisement

Privacy Preserving Location Recommendations

  • Shahriar BadshaEmail author
  • Xun Yi
  • Ibrahim Khalil
  • Dongxi Liu
  • Surya Nepal
  • Elisa Bertino
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10570)

Abstract

With the rapid development of location based social networks (LBSN) and location based services (LBS), the location recommendation to users has gained much attentions. A traditional location recommendation scheme may use any of the following information to generate a location recommendation: users’ check-in frequencies on different locations, their distance of other locations from any point of interest (POI), time of visiting different locations, social influence or interests on those locations which are visited by friends and so on. Depending on different contexts and tastes, results of recommending new location may vary. Again the users might have specific preferences of context to find the most suitable locations for him. However, these contextual information and preferences related to users are personal and an user usually does not want to reveal these information to any third party which are the main source of information to generate a recommendation. Revealing these information may cause to misuse or expose the data to third parties which is clearly breaching privacy of users. In this circumstances, it is essential to hide users’ check-in history in different locations from service providers, and get advantages of the server’s processing power to generate user personalized location recommendations. To address these challenges we present a cryptographic framework to preserve users’ privacy and simultaneously generating location recommendations for users. We also incorporate users’ friendship network along with the location preferences and show that users are able to choose their friends’ preferences on different locations to influence the recommendation results without revealing any information. The security and performance analysis show that the protocol is secure as well as practical.

Keywords

Homomorphic encryption Recommendations Location 

References

  1. 1.
    Beresford, A.R., Stajano, F.: Mix zones: user privacy in location-aware services. In: Proceedings of the Second IEEE Annual Conference on Pervasive Computing and Communications Workshops, pp. 127–131. IEEE (2004)Google Scholar
  2. 2.
    Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)Google Scholar
  3. 3.
    Gedik, B., Liu, L.: Location privacy in mobile systems: a personalized anonymization model. In: Proceedings of the 25th IEEE International Conference on Distributed Computing Systems, ICDCS 2005, pp. 620–629. IEEE (2005)Google Scholar
  4. 4.
    Ghinita, G., Kalnis, P., Kantarcioglu, M., Bertino, E.: Approximate and exact hybrid algorithms for private nearest-neighbor queries with database protection. GeoInformatica 15(4), 699–726 (2011)CrossRefGoogle Scholar
  5. 5.
    Ghinita, G., Kalnis, P., Khoshgozaran, A., Shahabi, C., Tan, K.L.: Private queries in location based services: anonymizers are not necessary. In: Proceedings of the 2008 ACM SIGMOD International Conference on Management of Data, pp. 121–132. ACM (2008)Google Scholar
  6. 6.
    Ghinita, G., Kalnis, P., Skiadopoulos, S.: Prive: anonymous location-based queries in distributed mobile systems. In: Proceedings of the 16th International Conference on World Wide Web, pp. 371–380. ACM (2007)Google Scholar
  7. 7.
    Golle, P., Partridge, K.: On the anonymity of home/work location pairs. In: Tokuda, H., Beigl, M., Friday, A., Brush, A.J.B., Tobe, Y. (eds.) Pervasive 2009. LNCS, vol. 5538, pp. 390–397. Springer, Heidelberg (2009). doi: 10.1007/978-3-642-01516-8_26 CrossRefGoogle Scholar
  8. 8.
    Gruteser, M., Grunwald, D.: Anonymous usage of location-based services through spatial and temporal cloaking. In: Proceedings of the 1st International Conference on Mobile Systems, Applications and Services, pp. 31–42. ACM (2003)Google Scholar
  9. 9.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Enhancing security and privacy in traffic-monitoring systems. IEEE Pervasive Comput. 5(4), 38–46 (2006)CrossRefGoogle Scholar
  10. 10.
    Hoh, B., Gruteser, M., Xiong, H., Alrabady, A.: Preserving privacy in GPS traces via uncertainty-aware path cloaking. In: Proceedings of the 14th ACM Conference on Computer and Communications Security, pp. 161–171. ACM (2007)Google Scholar
  11. 11.
    Jiang, T., Wang, H.J., Hu, Y.C.: Preserving location privacy in wireless lans. In: Proceedings of the 5th International Conference on Mobile Systems, Applications and Services, pp. 246–257. ACM (2007)Google Scholar
  12. 12.
    Kalnis, P., Ghinita, G., Mouratidis, K., Papadias, D.: Preventing location-based identity inference in anonymous spatial queries. IEEE Trans. Knowl. Data Eng. 19(12), 1719–1733 (2007)CrossRefGoogle Scholar
  13. 13.
    Krumm, J.: Inference attacks on location tracks. In: LaMarca, A., Langheinrich, M., Truong, K.N. (eds.) Pervasive 2007. LNCS, vol. 4480, pp. 127–143. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-72037-9_8 CrossRefGoogle Scholar
  14. 14.
    Mokbel, M.F., Chow, C.Y., Aref, W.G.: The new casper: query processing for location services without compromising privacy. In: Proceedings of the 32nd International Conference on Very Large Data Bases, pp. 763–774. VLDB Endowment (2006)Google Scholar
  15. 15.
    Naor, M., Pinkas, B.: Oblivious transfer with adaptive queries. In: Wiener, M. (ed.) CRYPTO 1999. LNCS, vol. 1666, pp. 573–590. Springer, Heidelberg (1999). doi: 10.1007/3-540-48405-1_36 CrossRefGoogle Scholar
  16. 16.
    Ostrovsky, R., Skeith III, W.E.: A survey of single-database private information retrieval: techniques and applications. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 393–411. Springer, Heidelberg (2007). doi: 10.1007/978-3-540-71677-8_26 CrossRefGoogle Scholar
  17. 17.
    Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). doi: 10.1007/3-540-48910-X_16 CrossRefGoogle Scholar
  18. 18.
    Papadopoulos, S., Bakiras, S., Papadias, D.: Nearest neighbor search with strong location privacy. Proc. VLDB Endowment 3(1–2), 619–629 (2010)CrossRefGoogle Scholar
  19. 19.
    Paulet, R., Kaosar, M.G., Yi, X., Bertino, E.: Privacy-preserving and content-protecting location based queries. IEEE Trans. Knowl. Data Eng. 26(5), 1200–1210 (2014)CrossRefGoogle Scholar
  20. 20.
    Yiu, M.L., Jensen, C.S., Huang, X., Lu, H.: Spacetwist: managing the trade-offs among location privacy, query performance, and query accuracy in mobile services. In: IEEE 24th International Conference on Data Engineering, ICDE 2008, pp. 366–375. IEEE (2008)Google Scholar
  21. 21.
    Yu, Y., Chen, X.: A survey of point-of-interest recommendation in location-based social networks. In: Workshops at the Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Shahriar Badsha
    • 1
    Email author
  • Xun Yi
    • 1
  • Ibrahim Khalil
    • 1
  • Dongxi Liu
    • 2
  • Surya Nepal
    • 2
  • Elisa Bertino
    • 3
  1. 1.RMIT UniversityMelbourneAustralia
  2. 2.CSIROSydneyAustralia
  3. 3.Purdue UniversityWest LafayetteUSA

Personalised recommendations