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Benign Strategy for Recommended Location Service Based on Trajectory Data

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Data Science (ICPCSEE 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1058))

Abstract

A new collaborative filtered recommendation strategy oriented to trajectory data is proposed for communication bottlenecks and vulnerability in centralized system structure location services. In the strategy based on distributed system architecture, individual user information profiles were established using daily trajectory information and neighboring user groups were established using density measure. Then the trajectory similarity and profile similarity were calculated to recommend appropriate location services using collaborative filtering recommendation method. The strategy was verified on real position data set. The proposed strategy provides higher quality location services to ensure the privacy of user position information.

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References

  1. Singh, M.P., Yu, B., Venkatraman, M.: Community-based service location. Commun. ACM 44, 49–54 (2001)

    Article  Google Scholar 

  2. Grutester, 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 (MobiSys 2003), San Francisco, CA, USA, 5–8 May 2003, pp. 31–42 (2003)

    Google Scholar 

  3. Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 10, 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  4. To, H., Ghinita, G., Fan, L., Shahabi, C.: Differentially private location protection for worker datasets in spatial crowdsourcing. IEEE Trans. Mob. Comput. 16, 934–949 (2017)

    Google Scholar 

  5. Chow, C.Y., Mokbel, M.F.: Privacy in location-based services: a system architecture perspective. SIGSPATIAL Spec. 1, 23–27 (2009)

    Article  Google Scholar 

  6. Chow, C.Y., Mokbel, M.F., Liu, X.: Spatial cloaking for anonymous location-based services in mobile peer-to-peer environments. Geoinformatica 15, 351–380 (2011)

    Article  Google Scholar 

  7. Shokri, R., Theodorakopoulos, G., Papadimitratos, P., Kazemi, E., Hubaux, J.-P.: Hiding in mobile crowd: location privacy through collaboration. IEEE Trans. Dependable Secure Comput. 11, 266–279 (2014)

    Article  Google Scholar 

  8. Wang, P., Yang, J., Zhang, J.P.: Protection of location privacy based on distributed collaborative recommendations. PLoS One 11, e0163053 (2016)

    Article  Google Scholar 

  9. Zhang, W., Cui, X., Li, D., Yuan, D., Wang, M.: The location privacy protection research in location-based service. In: Proceedings of the IEEE International Conference on Geoinformatics, Beijing, China, 18–20 June 2010

    Google Scholar 

  10. Bayardo, R.J., Agrawal, R.: Data privacy through optimal k-anonymization. In: Proceedings of the IEEE 21st International Conference on Data Engineering (ICDE 2005), Tokoyo, Japan, 5–8 April 2005, pp. 217–228 (2005)

    Google Scholar 

  11. Kou, G., Peng, Y., Wang, G.: Evaluation of clustering algorithms for financial risk analysis using MCDM methods. Inf. Sci. 275, 1–12 (2014)

    Article  Google Scholar 

  12. Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344, 1492–1496 (2014)

    Article  Google Scholar 

  13. Cao, Y., Yoshikawa, M.: Differentially private real-time data publishing over infinite trajectory streams. IEICE Trans. Inf. Syst. E99-D(1), 68–73 (2016)

    Article  Google Scholar 

  14. Seidl, D.E., Jankowski, P., Tsou, M.H.: Privacy and spatial pattern preservation in masked GPS trajectory data. Int. J. Geogr. Inf. Sci. 30(4), 1–16 (2016)

    Article  Google Scholar 

  15. Wang, T., Zeng, J., Bhuiyan, M.Z.A., et al.: Trajectory privacy preservation based on a fog structure in cloud location services. IEEE Access 5, 7692–7701 (2017)

    Article  Google Scholar 

  16. Huo, Z., Meng, X., Hu, H., Huang, Y.: You Can Walk Alone: trajectory privacy-preserving through significant stays protection. In: Lee, S., Peng, Z., Zhou, X., Moon, Y.-S., Unland, R., Yoo, J. (eds.) DASFAA 2012. LNCS, vol. 7238, pp. 351–366. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29038-1_26

    Chapter  Google Scholar 

  17. Hwang, R.H., Hsueh, Y.L., Chung, H.W.: A novel time-obfuscated algorithm for trajectory privacy protection. IEEE Trans. Serv. Comput. 7(2), 126–139 (2014)

    Article  Google Scholar 

  18. Gao, S., Ma, J., Shi, W., et al.: LTPPM: a location and trajectory privacy protection mechanism in participatory sensing. Wirel. Commun. Mobile Comput. 15(1), 155–169 (2015)

    Article  Google Scholar 

  19. Amini, S., Janne, L., Hong, J., Lin, J., Norman, S., Toch, E.: Cache: caching location-enhanced content to improve user privacy. In: Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services (MobiSys 2011), Bethesda, MD, USA, 28 June–1 July 2011, pp. 197–210 (2011)

    Google Scholar 

  20. Shokri, R., Papadimitratos, P., Theodorakopoulos, G., Hubaux, J.P.: Collaborative location privacy. In: Proceedings of the IEEE 8th International Conference on Mobile Adhoc and Sensor Systems, Valencia, Spain, 17–22 October 2011

    Google Scholar 

  21. Chow, C., Mokbel, M.F., Liu, X.: A peer-to-peer spatial cloaking algorithm for anonymous location-based services. In: Proceedings of the ACM Symposium on Advances in Geographic Information Systems (ACM GIS 2006), Arlington, VA, USA, 10–11 November 2006, pp. 171–178 (2006)

    Google Scholar 

  22. Boutet, A., Frey, D., Guerraoui, R., Jégou, A., Kermarrec, A.M.: Privacy-preserving distributed collaborative filtering. Computing 98, 827–846 (2016)

    Article  MathSciNet  Google Scholar 

  23. Chen, K., Liu, L.: Privacy-preserving multiparty collaborative mining with geometric data perturbation. IEEE Trans. Parallel Distrib. Syst. 20, 1764–1776 (2009)

    Article  Google Scholar 

  24. Zhu, T., Ren, Y., Zhou, W., Rong, J., Xiong, P.: An effective privacy preserving algorithm for neighborhood-based collaborative filtering. Future Gener. Comput. Syst. 36, 142–155 (2014)

    Article  Google Scholar 

  25. Huang, Z., Zeng, D., Chen, H.: A comparison of collaborative-filtering recommendation algorithms for e-commerce. IEEE Intell. Syst. 22, 68–78 (2007)

    Article  Google Scholar 

  26. 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). https://doi.org/10.1007/3-540-48910-X_16

    Chapter  Google Scholar 

  27. Piorkowski, M., Sarafijanovic-Djukic, N., Grossglauser, M.: A parsimonious model of mobile partitioned networks with clustering. In: Proceedings of the First International Communication Systems and Networks and Workshops (COMSNETS 2009), Bangalore, India, 5–10 January 2009

    Google Scholar 

  28. Domingo-Ferrer, J., Trujillo-Rasua, R.: Microaggregation- and permutation-based anonymization of movement data. Inf. Sci. 208, 55–80 (2012)

    Article  Google Scholar 

Download references

Acknowledgments

We acknowledge the support of the National Natural Science Foundation of China under grant nos. 61672179, 61370083, 61402126; the Natural Science Foundation Heilongjiang Province of China under grant nos. F2015030; the Youths Science Foundation of Heilongjiang Province of China under grant no. QC2016083; the Heilongjiang Postdoctoral Science Foundation no. LBH-Z14071.

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Authors and Affiliations

  1. College of Computer Science and Technology, Harbin Engineering University, Harbin, Heilongjiang, China

    Jing Yang, Peng Wang & Jianpei Zhang

  2. College of Information Engineering, Suihua University, Suihua, Heilongjiang, China

    Peng Wang

Authors
  1. Jing Yang
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  2. Peng Wang
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  3. Jianpei Zhang
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Corresponding author

Correspondence to Peng Wang .

Editor information

Editors and Affiliations

  1. Guilin University of Technology, Guilin, China

    Xiaohui Cheng

  2. Northeast Forestry University, Harbin, China

    Weipeng Jing

  3. Harbin University of Science and Technology, Harbin, China

    Xianhua Song

  4. National Academy of Guo Ding Institute of Data Science, Harbin, China

    Zeguang Lu

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Yang, J., Wang, P., Zhang, J. (2019). Benign Strategy for Recommended Location Service Based on Trajectory Data. In: Cheng, X., Jing, W., Song, X., Lu, Z. (eds) Data Science. ICPCSEE 2019. Communications in Computer and Information Science, vol 1058. Springer, Singapore. https://doi.org/10.1007/978-981-15-0118-0_1

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  • DOI: https://doi.org/10.1007/978-981-15-0118-0_1

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-15-0117-3

  • Online ISBN: 978-981-15-0118-0

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