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A Privacy-Preserving Takeaway Delivery Service Scheme

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Provable and Practical Security (ProvSec 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 14217))

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Abstract

More and more applications based on location services continue to make our lives rich and convenient. However, location information may also quietly expose our privacy, such as work location, eating habits, etc. In this article, we have designed a solution to the problem of invisibly leaking our takeaway order information. In our scheme, users only need to submit a service request. The edge server is responsible for the service response and completes most calculations. The service platform generates orders based on the calculation results, sends them to the merchants and returns them, and selects suitable delivery men for the users. The proposed scheme uses non-interactive key exchange and secure Manhattan distance calculation to protect the location privacy of mobile users. Security analysis shows the proposed scheme is privacy-protected under our defined threat model. In addition, our program experiment proved to be feasible.

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References

  1. Shen, H., Li, J., Wu, G., Zhang, M.: Data release for machine learning via correlated differential privacy. Inf. Process. Manag. 60(3), 103349 (2023)

    Article  Google Scholar 

  2. Shen, H., Zhang, M., Shen, J.: Efficient privacy-preserving cube-data aggregation aggregation scheme for smart grids. IEEE Trans. Inf. Forensics Secur. 12(6), 1369–1381 (2017)

    Article  Google Scholar 

  3. Shen, H., Zhang, M., Wang, H., Guo, F., Susilo, W.: A lightweight privacy-preserving fair meeting location determination scheme. IEEE Internet Things J. 7(4), 3038–3093 (2020)

    Article  Google Scholar 

  4. Alrayes, F., Abdelmoty, A.I., El-Geresy, W.B., Theodorakopoulos, G.: Modelling perceived risks to personal privacy from location disclosure on online social networks. Int. J. Geogr. Inf. Sci. 34(1), 150–176 (2020)

    Article  Google Scholar 

  5. Dou, J., Ge, X., Wang, Y.: Secure Manhattan distance computation and its application. Chin. J. Comput. 43(2), 352–365 (2020)

    Google Scholar 

  6. Zhu, H., Wang, F., Lu, R., Liu, F., Fu, G., Li, H.: Efficient and privacy-preserving proximity detection schemes for social applications. IEEE Internet Things J. 5(4), 2947–2957 (2018)

    Article  Google Scholar 

  7. Sun, A., Zhao, G., Zhao, M., et al.: A sign(x) point in-out polygon test algorithm based on sign function and its application. Comput. Eng. Sci. 39(4), 785–790 (2017)

    Google Scholar 

  8. Ma, C., Zhang, Y.: An improved method for judging relationship between point and polygon based on cross product. Sci. Surv. Mapp. 38(1), 125–127 (2013)

    Google Scholar 

  9. Shen, C.: Q algorithm of point-in-polygon analysis. J. Yangzhou Univ. Nat. Sci. Ed. 4, 24–26 (1999)

    MATH  Google Scholar 

  10. Zhai, Y., Xu, W., Zhang, Q.: Judgment of topological relation between point and polygon or polyhedron. Comput. Eng. Des. 4, 972–976 (2015)

    Google Scholar 

  11. Liu, L.: An optimized algorithm to detemine topo-relation between point and polygon and clockwise or anti-clockwise in polygon. Geomat. Spatial Inf. Technol. 30(1), 84–86 (2007)

    Google Scholar 

  12. Dong, X., Liu, R.: New algorithm for determining position relation between simple polygon and point. Comput. Eng. Appl. 45(2), 185–186 (2009)

    Google Scholar 

  13. Zhang, L., He, F., Li, H.: A method for detecting points in polygons based on singular ray method, vol. 37. no. S2, pp. 133–135 (2020)

    Google Scholar 

  14. Shen, H., Zhang, M., Wang, H., Guo, F., Susilo, W.: A lightweight privacy-preserving fair meeting location determination scheme. IEEE Internet Things J. 7(4), 3083–3093 (2020)

    Article  Google Scholar 

  15. Sun, G., Song, L., Liao, D., Yu, H., Chang, V.: Towards privacy preservation for ‘check-in’ services in location-based social networks. Inf. Sci. 481, 616–634 (2019)

    Article  Google Scholar 

  16. Wang, N., Fu, J., Li, J., Bhargava, B.K.: Source-location privacy protection based on anonymity cloud in wireless sensor networks. IEEE Trans. Inf. Forensics Secur. 15, 100–114 (2020)

    Article  Google Scholar 

  17. Huang, K.L., Kanhere, S.S., Hu, W.: Preserving privacy in participatory sensing systems. Comput. Commun. 33(11), 1266–1280 (2010)

    Article  Google Scholar 

  18. Tian, S., Cai, Y., Zheng, Q.: A hybrid approach for privacy-preserving processing of knn queries in mobile database systems. In: 22nd ACM International Conference on Information and Knowledge Management, CIKM 2013, San Francisco, CA, USA, 27 October–1 November 2013, pp. 1161–1164 (2013)

    Google Scholar 

  19. Zhu, X., Chi, H., Niu, B., Zhang, W., Li, Z., Li, H.: MobiCache: when k-anonymity meets cache. In: 2013 IEEE Global Communications Conference, GLOBECOM 2013, Atlanta, GA, USA, 9–13 December 2013, pp. 820–825 (2013)

    Google Scholar 

  20. Andrés, M.E., Bordenabe, N.E., Chatzikokolakis, K., Palamidessi, C.: Geo-indistinguishability: differential privacy for location-based systems. In: 2013 ACM SIGSAC Conference on Computer and Communications Security, CCS 2013, Berlin, Germany, 4–8 November 2013, pp. 901–914 (2013)

    Google Scholar 

  21. Yi, X., Paulet, R., Bertino, E., Varadharajan, V.: Practical approximate k nearest neighbor queries with location and query privacy. IEEE Trans. Knowl. Data Eng. 28(6), 1546–1559 (2016)

    Article  Google Scholar 

  22. Shen, H., Wu, G., Xia, Z., Susilo, W., Zhang, M.: A privacy-preserving and Verifiable statistical analysis scheme for an E-Commerce platform. IEEE Trans. Inf. Forensics Secur. 18, 2637–2652 (2023)

    Article  Google Scholar 

  23. Sakai, R., Kasahara, M.: ID based cryptosystems with pairing on elliptic curve. IACR Cryptol. ePrint Arch., p. 54 (2003)

    Google Scholar 

  24. Shen, X., Wang, L., Pei, Q., Liu, Y., Li, M.: Location privacy-preserving in online taxi-hailing services. Peer-to-Peer Netw. Appl. 14(1), 69–81 (2021)

    Article  Google Scholar 

  25. Bilogrevic, I., Jadliwala, M., Joneja, V., Kalkan, K., Hubaux, J.-P., Aad, I.: Privacy-preserving optimal meeting location determination on mobile devices. IEEE Trans. Inf. Forensics Secur. 9(7), 1141–1156 (2014)

    Article  Google Scholar 

  26. Kalkan, S., Kaya, K., Selcuk, A.A.: Generalized ID-based ElGamal signatures. In: 22nd International Symposium on Computer and Information Sciences, Ankara, Turkey, 7–9 November 2007, pp. 1–6 (2007)

    Google Scholar 

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Acknowledgements

This work is supported in part by the National Natural Science Foundation of China (61702168) and in part by the Green Industry Technology Leading Program of Hubei University of Technology (XJ2021000901).

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Authors and Affiliations

  1. Hubei University of Technology, Wuhan, 430068, Hubei, China

    Lang Xu, Jiqiang Li, Hao Zhang & Hua Shen

Authors
  1. Lang Xu
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  2. Jiqiang Li
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  3. Hao Zhang
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  4. Hua Shen
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Corresponding authors

Correspondence to Hao Zhang or Hua Shen .

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Editors and Affiliations

  1. Hubei University of Technology, Wuhan, China

    Mingwu Zhang

  2. Hong Kong Polytechnic University, Kowloon, Hong Kong

    Man Ho Au

  3. University of Wollongong, Wollongong, NSW, Australia

    Yudi Zhang

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Xu, L., Li, J., Zhang, H., Shen, H. (2023). A Privacy-Preserving Takeaway Delivery Service Scheme. In: Zhang, M., Au, M.H., Zhang, Y. (eds) Provable and Practical Security. ProvSec 2023. Lecture Notes in Computer Science, vol 14217. Springer, Cham. https://doi.org/10.1007/978-3-031-45513-1_21

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  • DOI: https://doi.org/10.1007/978-3-031-45513-1_21

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45512-4

  • Online ISBN: 978-3-031-45513-1

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