Skip to main content
Springer Nature Link
Log in

Privacy Preserving Adjacency Query Supporting Homoionym Search over Medical Graph Data in Cloud Computing

  • Conference paper
  • First Online:
CLOUD Computing – CLOUD 2024 (CLOUD 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15423))

Included in the following conference series:

  • 71 Accesses

Abstract

Cloud computing is widely used in all walks of life today. Massive amounts of medical graph data are being outsourced to cloud servers to reduce overhead. The untrustworthiness of cloud servers puts the sensitive information of outsourced graph data at risk. To eliminate this security risk, it is an effective method to encrypt sensitive data. The adjacent queries are frequently used and highly valuable in graph data operations, and the adjacency query supporting homoionym search will enlarge the query effect and improve the query function. When the medical graph data is encrypted and stored on the cloud server, the operation of the data becomes extremely difficult. In this article, we propose a scheme to implement the adjacency query supporting homoionym search in cloud computing (AQHS), which maintains search contents privacy. We use a stem extraction algorithm and the searchable encryption mechanism to build secure index, and then achieve the adjacency query. The security of our proposed scheme is verified by formal analysis, and the effectiveness of the scheme is verified by experimental analysis.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Sampe, J., Artigas, M.S., Vernik, G., et al.: Outsourcing data processing jobs with Lithops. IEEE Trans. Cloud Comput. 11(1), 1026–1037 (2023)

    Article  Google Scholar 

  2. Li, J., Ye, H., et al.: Efficient and secure outsourcing of differentially private data publishing with multiple evaluators. IEEE Trans. Dependable Sec. Comput. 19(1), 109–121 (2022)

    Article  Google Scholar 

  3. Ren, Y., Song, Z., Sun, S., et al.: Outsourcing lda-based face recognition to an untrusted cloud. IEEE Trans. Dependable Sec. Comput. 23(3), 2058–2070 (2023)

    Google Scholar 

  4. Liu, W., Wen, D., Wang, H., et al.: Skyline nearest neighbor search on multi-layer graphs. In: 2014 IEEE 35th International Conference on Data Engineering Workshops, pp. 259-265. IEEE, Piscataway, N.J., USA (2019)

    Google Scholar 

  5. Potamias, M., Bonchi, F., Gionis, A., et al.: K-nearest neighbors in uncertain graphs. Proc/ VLDB Endowment 3(1), 997–1008 (2010)

    Article  Google Scholar 

  6. Wang, R., Yan, J., Yang, X., et al.: Combinatorial learning of robust deep graph matching: an embedding based approach. IEEE Trans. Pattern Anal. Mach. Intell. 45(6), 6984–7000 (2023)

    Article  Google Scholar 

  7. Bazgan, C., Pontoizeau, T., Tuza, Z., et al.: Finding a potential community in networks. Theoret. Comput. Sci. 769, 32–42 (2019)

    Article  MathSciNet  Google Scholar 

  8. Ferrer-Cid, P., Barceló-Ordinas, J., García-Vidal, J., et al.: Volterra graph-based outlier detection for air pollution sensor networks. IEEE Trans. Netw. Sci. Eng. 9(4), 2759–2771 (2023)

    Article  Google Scholar 

  9. Li, X., Ye, H., Li, T., et al.: Efficient and secure outsourcing of differentially private data publishing with multiple evaluators. IEEE Trans. Dependable Secure Comput. 19(1), 67–76 (2022)

    Article  Google Scholar 

  10. Zhang, X., Zhao, J., Xu, C., et al.: DOPIV: post-quantum secure identity-based data outsourcing with public integrity verification in cloud storage. IEEE Trans. Serv. Comput. 15(1), 334–345 (2022)

    Article  Google Scholar 

  11. Song, D.X., Wagner, D., Perrig, A.: Practical techniques for searches on encrypted data. In: Proceeding: IEEE Symposium on Security and Privacy (S &P 2000), vol. 2000, pp. 44–55. IEEE: Los Alamitos, CA, USA (2000)

    Google Scholar 

  12. Chang, Y.-C., Mitzenmacher, M.: Privacy preserving keyword searches on remote encrypted data. In: Ioannidis, J., Keromytis, A., Yung, M. (eds.) ACNS 2005. LNCS, vol. 3531, pp. 442–455. Springer, Heidelberg (2005). https://doi.org/10.1007/11496137_30

    Chapter  Google Scholar 

  13. Goh, E.J.: Secure indexes. In: Cryptology ePrint Archive, Report 2003/216 (2003)

    Google Scholar 

  14. Curtmola, R., Garay, J., Kamara, S., et al.: Searchable symmetric encryption: improved definitions and efficient constructions. In: Proceedings of the 13th ACM Conference on Computer and Communications Security (CCS 2006), pp. 79-88. ACM, Alexandria, VA, United States (2006)

    Google Scholar 

  15. Baek, J., Safavi-Naini, R., Susilo, W.: Public key encryption with keyword search revisited. In: Gervasi, O., Murgante, B., Laganà, A., Taniar, D., Mun, Y., Gavrilova, M.L. (eds.) ICCSA 2008. LNCS, vol. 5072, pp. 1249–1259. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69839-5_96

    Chapter  Google Scholar 

  16. Cao, N., Wang, C., Li, M., et al.: Privacy-preserving multi-keyword ranked search over encrypted cloud data. In: IEEE Conference on Computer Communications (INFOCOM 2011), pp. 829-837. IEEE: Shanghai, China (2011)

    Google Scholar 

  17. Mei, Z., Yu, J., Zhang, C., et al.: Secure multi-dimensional data retrieval with access control and range query in the cloud. Inf. Syst. 122, 102343 (2024)

    Article  Google Scholar 

  18. Wang, C., Ren, K., Yu, S., et al. Achieving usable and privacy-assured similarity search over outsourced cloud data. In: IEEE Conference on Computer Communications (INFOCOM 2012), pp. 451-459. IEEE: Orlando, FL, USA (2012)

    Google Scholar 

  19. Chase, M., Kamara, S., et al.: Structured encryption and controlled disclosure. structured encryption and controlled disclosure. Cryptol. Inform. Sec. 2010, 577-594 (2010)

    Google Scholar 

  20. Cao, N., Yang, Z., Wang, C., et al.: Privacy-preserving query over encrypted graph-structured data in cloud computing. In: Proceedings of the 2011 31st International Conference on Distributed Computing Systems (ICDCS 2011), pp. 393 - 402. IEEE, Los Alamitos, CA, USA (2011)

    Google Scholar 

  21. Shen, M., Ma, B., Zhu, L., et al.: Cloud-based approximate constrained shortest distance queries over encrypted graphs with privacy protection. IEEE Trans. Inf. Forensics Secur. 13, 940–953 (2018)

    Article  Google Scholar 

  22. Ciucanu, R., Lafourcade, P.: \(\sf GOOSE\): a secure framework for graph outsourcing and SPARQL evaluation. In: Singhal, A., Vaidya, J. (eds.) DBSec 2020. LNCS, vol. 12122, pp. 347–366. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-49669-2_20

    Chapter  Google Scholar 

  23. Katz, J., Lindell, Y.: Introduction to Modern Cryptography. Chapman & Hall/CRC (2007)

    Google Scholar 

  24. Singhal, A.: Modern information retrieval: a brief overview. Bull. IEEE Comput. Soc. Tech. Committee Data Eng. 24(4), 35–43 (2001)

    Google Scholar 

  25. Leskovec, J., Lang, K.J., Dasgupta, A., et al.: Community structure in large networks: natural cluster sizes and the absence of large well-defined clusters. Internet Math. 6(1), 29–123 (2009)

    Article  MathSciNet  Google Scholar 

  26. Klimt, B., Yang, Y.: Introducing the Enron corpus. In: First Conference on Email and Anti-Spam (CEAS 2004), pp. 1-2. Google, Microsoft, etc.: Mountain View, CA, USA (2004)

    Google Scholar 

Download references

Acknowledgment

The authors gratefully acknowledge the editor and the reviewers’ comments and helpful suggestions. This research is supported in part by the National Nature Science Foundation of China (No. 62262033 and 62062045).

Author information

Authors and Affiliations

  1. School of Computer and Big Data Science, Jiujiang University, Jiujiang, 332005, China

    Hourong Li, Yun Tian, Bin Wu & Jiaoli Shi

  2. Jiujiang Key Laboratory of Network and Information Security, Jiujiang, 332005, China

    Bin Wu & Jiaoli Shi

Authors
  1. Hourong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yun Tian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jiaoli Shi
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Wu .

Editor information

Editors and Affiliations

  1. Shenzhen Institute of Advanced Technology, Shenzhen, China

    Yang Wang

  2. Shenzhen University, Shenzhen, China

    Liang-Jie Zhang

Rights and permissions

Reprints and permissions

Copyright information

© 2025 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, H., Tian, Y., Wu, B., Shi, J. (2025). Privacy Preserving Adjacency Query Supporting Homoionym Search over Medical Graph Data in Cloud Computing. In: Wang, Y., Zhang, LJ. (eds) CLOUD Computing – CLOUD 2024. CLOUD 2024. Lecture Notes in Computer Science, vol 15423. Springer, Cham. https://doi.org/10.1007/978-3-031-77153-8_5

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-77153-8_5

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-77152-1

  • Online ISBN: 978-3-031-77153-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation