SecReach: Secure Reachability Computation on Encrypted Location Check-in Data

  • Hanyu Quan
  • Boyang Wang
  • Iraklis Leontiadis
  • Ming LiEmail author
  • Yuqing Zhang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10052)


Reachability, which answers whether one person is reachable from another through a sequence of contacts within a period of time, is of great importance in many domains such as social behavior analysis. Recently, with the prevalence of various location-based services (LBSs), a great amount of spatiotemporal location check-in data is generated by individual GPS-equipped mobile devices and collected by LBS companies, which stimulates research on reachability queries in these location check-in datasets. Meanwhile, a growing trend is for LBS companies to use scalable and cost-effective clouds to collect, store, and analyze data, which makes it necessary to encrypt location check-in data before outsourcing due to privacy concerns. In this paper, for the first time, we propose a scheme, SecReach, to securely evaluate reachability queries on encrypted location check-in data by using somewhat homomorphic encryption (SWHE). We prove that our scheme is secure against a semi-honest cloud server. We also present a proof-of-concept implementation using the state-of-the-art SWHE library (i.e., HElib), which shows the efficiency and feasibility of our scheme.


Reachability Location privacy Homomorphic encryption 



We would like to thank the anonymous reviewers for their valuable comments. This work was supported by the US NSF grant CNS-1218085, the 111 Project of China (No. B16037), the National Natural Science Foundation of China (No.61272481, No. 61572460), the National Key Research and Development Plan of China (No. 2016YFB0800703), and the China Scholarship Council.


  1. 1.
    Bloom, B.H.: Space/time trade-offs in hash coding with allowable errors. Commun. ACM 13(7), 422–426 (1970)CrossRefzbMATHGoogle Scholar
  2. 2.
    Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (leveled) fully homomorphic encryption without bootstrapping. In: Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, pp. 309–325. ACM (2012)Google Scholar
  3. 3.
    Cheon, J.H., Kim, M., Lauter, K.: Homomorphic computation of edit distance. In: Brenner, M., Christin, N., Johnson, B., Rohloff, K. (eds.) FC 2015 Workshops. LNCS, vol. 8976, pp. 194–212. Springer, Heidelberg (2015)CrossRefGoogle Scholar
  4. 4.
    Dai, W., Sunar, B.: cuHE: a homomorphic encryption accelerator library. In: Pasalic, E., Knudsen, L.R. (eds.) BalkanCryptSec 2015. LNCS, vol. 9540, pp. 169–186. Springer, Heidelberg (2016). doi: 10.1007/978-3-319-29172-7_11 CrossRefGoogle Scholar
  5. 5.
    De Montjoye, Y.A., Hidalgo, C.A., Verleysen, M., Blondel, V.D.: Unique in the crowd: The privacy bounds of human mobility. Sci. Rep. 3 (2013)Google Scholar
  6. 6.
    Elmehdwi, Y., Samanthula, B.K., Jiang, W.: Secure k-nearest neighbor query over encrypted data in outsourced environments. In: 2014 IEEE 30th International Conference on Data Engineering, pp. 664–675. IEEE (2014)Google Scholar
  7. 7.
    Gentry, C.: A fully homomorphic encryption scheme. Ph.D. thesis, Stanford University (2009)Google Scholar
  8. 8.
    Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014, Part I. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  9. 9.
    Katz, J., Lindell, Y.: Introduction to Modern Cryptography, 2nd edn. CRC Press, Boca Raton (2014)zbMATHGoogle Scholar
  10. 10.
    Khedr, A., Gulak, G.: Securemed: Secure medical computation using gpu-accelerated homomorphic encryption scheme. Cryptology ePrint Archive, Report 2016/445 (2016)Google Scholar
  11. 11.
    Li, M., Zhu, H., Gao, Z., Chen, S., Yu, L., Hu, S., Ren, K.: All your location are belong to us: Breaking mobile social networks for automated user location tracking. In: Proceedings of the 15th ACM International Symposium on Mobile Ad Hoc Networking and Computing, pp. 43–52. ACM (2014)Google Scholar
  12. 12.
    Liu, A., Zhengy, K., Liz, L., Liu, G., Zhao, L., Zhou, X.: Efficient secure similarity computation on encrypted trajectory data. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 66–77. IEEE (2015)Google Scholar
  13. 13.
    Liu, X., Wang, B., Yang, X.: Efficiently anonymizing social networks with reachability preservation. In: Proceedings of the 22nd ACM International Conference on Information and Knowledge Management, pp. 1613–1618. ACM (2013)Google Scholar
  14. 14.
    Naehrig, M., Lauter, K., Vaikuntanathan, V.: Can homomorphic encryption be practical? In: Proceedings of the 3rd ACM workshop on Cloud computing security workshop. pp. 113–124. ACM (2011)Google Scholar
  15. 15.
    Narayanan, A., Thiagarajan, N., Lakhani, M., Hamburg, M., Boneh, D.: Location privacy via private proximity testing. In: NDSS (2011)Google Scholar
  16. 16.
    Peikert, C.: A decade of lattice cryptography. Cryptology ePrint Archive, Report 2015/939 (2015).
  17. 17.
    Samanthula, B.K., Elmehdwi, Y., Jiang, W.: K-nearest neighbor classification over semantically secure encrypted relational data. IEEE Trans. Knowl. Data Eng. 27(5), 1261–1273 (2015)CrossRefGoogle Scholar
  18. 18.
    Shahabi, C., Fan, L., Nocera, L., Xiong, L., Li, M.: Privacy-preserving inference of social relationships from location data: a vision paper. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, p. 9. ACM (2015)Google Scholar
  19. 19.
    Shirani-Mehr, H., Banaei-Kashani, F., Shahabi, C.: Efficient reachability query evaluation in large spatiotemporal contact datasets. Proc. VLDB Endowment 5(9), 848–859 (2012)CrossRefGoogle Scholar
  20. 20.
    Shokri, R., Theodorakopoulos, G., Le Boudec, J.Y., Hubaux, J.P.: Quantifying location privacy. In: 2011 IEEE Symposium on Security and Privacy, pp. 247–262. IEEE (2011)Google Scholar
  21. 21.
    Strzheletska, E.V., Tsotras, V.J.: RICC: fast reachability query processing on large spatiotemporal datasets. In: Claramunt, C., Schneider, M., Wong, R.C.-W., Xiong, L., Loh, W.-K., Shahabi, C., Li, K.-J. (eds.) SSTD 2015. LNCS, vol. 9239, pp. 3–21. Springer, Heidelberg (2015). doi: 10.1007/978-3-319-22363-6_1 CrossRefGoogle Scholar
  22. 22.
    Sweeney, L.: k-anonymity: a model for protecting privacy. Int. J. Uncert. Fuzz. Knowl.-Based Syst. 10(05), 557–570 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  23. 23.
    Von Arb, M., Bader, M., Kuhn, M., Wattenhofer, R.: Veneta: serverless friend-of-friend detection in mobile social networking. In: 2008 IEEE International Conference on Wireless and Mobile Computing, Networking and Communications, pp. 184–189. IEEE (2008)Google Scholar
  24. 24.
    Wang, B., Li, M., Wang, H.: Geometric range search on encrypted spatial data. IEEE Trans. Inf. Forensics Secur. 11(4), 704–719 (2016)Google Scholar
  25. 25.
    Wang, B., Li, M., Wang, H., Li, H.: Circular range search on encrypted spatial data. In: 2015 IEEE Conference on Communications and Network Security (CNS), pp. 182–190. IEEE (2015)Google Scholar
  26. 26.
    Wang, S., Zhang, Y., Dai, W., Lauter, K., Kim, M., Tang, Y., Xiong, H., Jiang, X.: Healer: Homomorphic computation of exact logistic regression for secure rare disease variants analysis in gwas. Bioinformatics 32(2), 211–218 (2016)Google Scholar
  27. 27.
    Yi, P., Fan, Z., Yin, S.: Privacy-preserving reachability query services for sparse graphs. In: 2014 IEEE 30th International Conference on Data Engineering Workshops (ICDEW), pp. 32–35. IEEE (2014)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Hanyu Quan
    • 1
    • 2
  • Boyang Wang
    • 2
  • Iraklis Leontiadis
    • 2
  • Ming Li
    • 2
    Email author
  • Yuqing Zhang
    • 1
    • 3
  1. 1.School of Cyber EngineeringXidian UniversityXi’anChina
  2. 2.Department of Electrical and Computer EngineeringThe University of ArizonaTucsonUSA
  3. 3.University of Chinese Academy of SciencesBeijingChina

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