Skip to main content
SpringerLink
Log in

Secure shortest distance queries over encrypted graph in cloud computing

  • Original Paper
  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Graph databases have received increased interests as many applications are handled as graph problems. Shortest distance queries are one of the fundamental operations and have been studied for recent years. To ensure the data and query privacy, researchers have introduced some secure graph encryption schemes which support the shortest distance queries on a large-scale graph database. Unfortunately, most of them only provide an approximate result by pre-computing and storing a distance oracle. To provide the exact shortest path, our solution employs a distributed two-trapdoor public-key crypto-system to perform addition and comparison operations over ciphertexts. The detailed security analysis indicates that our scheme achieves semantically secure under DDH assumption and the experiments are performed on various real-world database and random database. The experimental result shows the feasibility of our scheme.

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

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Algorithm 1
Algorithm 2
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Data availability statements

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Notes

  1. SNAP. https://snap.stanford.edu/data/

References

  1. Sarwat, M., Elnikety, S., He, Y., & Kliot, G. (2012). Horton: Online query execution engine for large distributed graphs. In: Proceedings of IEEE 28th International Conference on Data Engineering (ICDE), Washington, DC, USA, 1-5 April, 2012, pp. 1289–1292

  2. Low, Y., Gonzalez, J., Kyrola, A., Bickson, D., Guestrin, C., & Hellerstein, J.M. (2010). Graphlab: A new framework for parallel machine learning. In: Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence (UAI), Catalina Island, CA, USA, July 8–11, 2010, pp. 340–349

  3. Han, W., Lee, S., Park, K., Lee, J., Kim, M., Kim, J., & Yu, H. (2013). Turbograph: a fast parallel graph engine handling billion-scale graphs in a single PC. In: Proceedings of 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2013, Chicago, IL, USA, August 11–14, 2013, pp. 77–85

  4. Kyrola, A., & Guestrin, C. (2014). Graphchi-db: Simple design for a scalable graph database system - on just a PC. CoRR abs/1403.0701

  5. Chase, M., & Kamara, S. (2010). Structured encryption and controlled disclosure. In: Proceedings of 16th International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT), Singapore, December 5–9, 2010, vol. 6477, pp. 577–594

  6. Meng, X., Kamara, S., Nissim, K., & Kollios, G. (2015). GRECS: graph encryption for approximate shortest distance queries. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security (CCS), Denver, CO, USA, October 12–16, 2015, pp. 504–517

  7. Wang, Q., Ren, K., Du, M., Li, Q., & Mohaisen, A. (2017). Secgdb: Graph encryption for exact shortest distance queries with efficient updates. In: Proceedings of 21st International Conference on Financial Cryptography and Data Security (FC), Sliema, Malta, April 3–7, 2017, vol. 10322, pp. 79–97

  8. Liu, X., Deng, R. H., Choo, K. R., & Weng, J. (2016). An efficient privacy-preserving outsourced calculation toolkit with multiple keys. IEEE Transactions on Information Forensics and Security, 11(11), 2401–2414.

    Article  Google Scholar 

  9. Katz, J., & Lindell, Y. (2014). Introduction to modern cryptography (2nd ed.). Boca Raton: CRC Press.

    Book  Google Scholar 

  10. Bresson, E., Catalano, D., & Pointcheval, D. (2003). A simple public-key cryptosystem with a double trapdoor decryption mechanism and its applications. In: Proceedings of 9th International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT), Taipei, Taiwan, November 30–December 4, 2003, vol. 2894, pp. 37–54

  11. Katz, J., Ostrovsky, R., & Yung, M. (2001). Efficient password authenticated key exchange using human-memorable passwords. In: Proceedings of International Conference on Theory and Application of Cryptographic Techniques, Innsbruck, Austria, May 6–10, 2001, pp. 457–494

  12. Katz, J., MacKenzie, P., Taban, G., & Gligor, V. (2005). Efficient password authenticated key exchange using human-memorable passwords. In: Proceedings of International Conference on Applied Cryptography and Network, New York, USA, June 7–10, 2005, pp. 1–16

  13. Yi, X., Ling, S., & Wang, H. (2013). Efficient two-server password-only authenticated key exchange. IEEE Transactions on Parallel Distributed System, 24(9), 1773–1782.

    Article  MathSciNet  Google Scholar 

  14. Curtmola, R., Garay, J.A., Kamara, S., & Ostrovsky, R. (2006). Searchable symmetric encryption: improved definitions and efficient constructions. In: Proceedings of the 13th ACM Conference on Computer and Communications Security (CCS), Alexandria, VA, USA, October 30–November 3, 2006, pp. 79–88

  15. Cash, D., Jarecki, S., Jutla, C.S., Krawczyk, H., Rosu, M., & Steiner, M. (2013). Highly-scalable searchable symmetric encryption with support for boolean queries. In: Proceedings of 33rd Annual Cryptology Conference (CRYPTO), Santa Barbara, CA, USA, August 18–22, 2013, vol. 8042, pp. 353–373

  16. Hamlin, A., Shelat, A., Weiss, M., & Wichs, D. (2018). Multi-key searchable encryption, revisited. In: Proceedings of 21st IACR International Conference on Practice and Theory of Public-Key Cryptography (PKC), Rio de Janeiro, Brazil, March 25–29, 2018, vol. 10769, pp. 95–124

  17. Liu, X., Deng, R. H., Choo, K. R., & Yang, Y. (2020). Privacy-preserving outsourced support vector machine design for secure drug discovery. IEEE Transactions on Cloud Computing, 8(2), 610–622.

    Article  Google Scholar 

  18. Barker, E., Barker, E., Burr, W., Polk, W., Smid, M., et al. (2006). Recommendation for key management: Part 1: General. Technology Administration, New York: National Institute of Standards and Technology.

    Book  Google Scholar 

  19. Liu, K., & Terzi, E. (2008). Towards identity anonymization on graphs. In: Proceedings of the ACM SIGMOD International Conference on Management of Data (SIGMOD), Vancouver, BC, Canada, June 10–12, 2008, pp. 93–106

  20. Cheng, J., Fu, A.W., & Liu, J. (2010). K-isomorphism: privacy preserving network publication against structural attacks. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Indianapolis, Indiana, USA, June 6–10, 2010, pp. 459–470

  21. Gao, J., Yu, J.X., Jin, R., Zhou, J., Wang, T., & Yang, D. (2011). Neighborhood-privacy protected shortest distance computing in cloud. In: Proceedings of the ACM International Conference on Management of Data (SIGMOD), Athens, Greece, June 12–16, 2011, pp. 409–420

  22. Dwork, C., McSherry, F., Nissim, K., & Smith, A.D. (2006). Calibrating noise to sensitivity in private data analysis. In: Proceedings of 3rd International Conference on Theory of Cryptography (TCC), New York, NY, USA, March 4–7, 2006, vol. 3876, pp. 265–284

  23. Shen, E., & Yu, T. (2013). Mining frequent graph patterns with differential privacy. In: Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) 2013, Chicago, IL, USA, August 11–14, 2013, pp. 545–553

  24. Kasiviswanathan, S.P., Nissim, K., Raskhodnikova, S., & Smith, A.D. (2013). Analyzing graphs with node differential privacy. In: Proceedings of the 10th International Conference on Theory of Cryptography (TCC), Tokyo, Japan, March 3–6, 2013, vol. 7785, pp. 457–476

  25. Mouratidis, K., & Yiu, M. L. (2012). Shortest path computation with no information leakage. Proceedings of the VLDB Endowment, 5(8), 692–703.

    Article  Google Scholar 

  26. Gentry, C. (2009). Fully homomorphic encryption using ideal lattices. In: Proceedings of 41st Annual ACM Symposium on Theory of Computing (STOC), Bethesda, MD, USA, May 31–June 2, 2009, pp. 169–178

  27. Aly, A., Cuvelier, E., Mawet, S., Pereira, O., & Vyve, M.V. (2013). Securely solving simple combinatorial graph problems. In: Proceedings of 17th International Conference on Financial Cryptography and Data Security (FC), Okinawa, Japan, April 1–5, 2013, vol. 7859, pp. 239–257

  28. Blanton, M., Steele, A., & Aliasgari, M. (2013). Data-oblivious graph algorithms for secure computation and outsourcing. In: Proceedings of 8th ACM Symposium on Information, Computer and Communications Security, (ASIACCS), Hangzhou, China—May 08–10, 2013, pp. 207–218

  29. Keller, M., & Scholl, P. (2014). Efficient, oblivious data structures for MPC. In: Proceedings of 20th International Conference on the Theory and Application of Cryptology and Information Security (ASIACRYPT) 2014, Kaoshiung, Taiwan, R.O.C., December 7–11, 2014, vol. 8874, pp. 506–525

  30. Gupta, D., Segal, A., Panda, A., Segev, G., Schapira, M., Feigenbaum, J., Rexford, J., & Shenker, S. (2012). A new approach to interdomain routing based on secure multi-party computation. In: Proceedings of 11th ACM Workshop on Hot Topics in Networks, Redmond, WA, USA—October 29–30, 2012, pp. 37–42

  31. Bayatbabolghani, F., Blanton, M., Aliasgari, M., & Goodrich, M.T. (2017). Secure fingerprint alignment and matching protocols. CoRR abs/1702.03379

  32. Shen, M., Ma, B., Zhu, L., Mijumbi, R., Du, X., & Hu, J. (2018). Cloud-based approximate constrained shortest distance queries over encrypted graphs with privacy protection. IEEE Transactions on Information Forensics and Security, 13(4), 940–953.

    Article  Google Scholar 

  33. Wu, D.J., Zimmerman, J., Planul, J., & Mitchell, J.C. (2016). Privacy-preserving shortest path computation. In: Proceedings of 23rd Annual Network and Distributed System Security Symposium (NDSS), San Diego, California, USA, February 21–24, 2016

  34. Zhang, C., Zhu, L., Xu, C., Sharif, K., Zhang, C., & Liu, X. (2020). PGAS: privacy-preserving graph encryption for accurate constrained shortest distance queries. Information Sciences, 506, 325–345.

    Article  MathSciNet  Google Scholar 

  35. Chen, Y., Ku, H., & Zhang, M. (2021). PP-OCQ: A distributed privacy-preserving optimal closeness query scheme for social networks. Computer Standards and Interfaces, 74, 103484.

    Article  Google Scholar 

  36. Liu, C., Zhu, L., He, X., & Chen, J. (2021). Enabling privacy-preserving shortest distance queries on encrypted graph data. IEEE Transactions on Dependable and Secure Computing, 18(1), 192–204.

    Article  ADS  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. School of Cybersecurity, Northwestern Polytechnical University, Xi’an, 710072, Shaanxi, China

    Jingjing Guo

  2. KU Leuven, Leuven, Belgium

    Jiacong Sun

Authors
  1. Jingjing Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jiacong Sun
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jingjing Guo.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Guo, J., Sun, J. Secure shortest distance queries over encrypted graph in cloud computing. Wireless Netw (2024). https://doi.org/10.1007/s11276-024-03692-7

Download citation

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s11276-024-03692-7

Keywords

Search

Navigation