Skip to main content
SpringerLink
Log in

An effective method for the protection of user health topic privacy for health information services

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

With the rapid development of emerging network technologies such as cloud computing, the background server-side of public health information services is widely deployed on the untrusted cloud, which has become one of the main threats of user health privacy leakage. To this end, this paper proposes an agent-based algorithm for the protection for user privacy health topics based on identity replacement. Its basic idea is to deploy a group of intermediate agents between the server-side and the client-side, to replace the identity of each health service request issued by client users and then submit it to the server-side, thereby, making it difficult to identify the real user corresponding to each request, and then improving the security of user health topic privacy on the completely-untrusted server-sides. Then, this paper proposes a client-based algorithm for the selection of intermediate agents, which evenly distributes the request data issued by client users to all the agents after topic identification and privacy computation for the request data, to improve the security of user health topic privacy on the incompletely-trusted agent-side. Finally, both theoretical analysis and experimental evaluation demonstrate the effectiveness of the proposed method, i.e., it can effectively improve the security of user privacy health topics on the untrusted server-side, under the premises of no changes to user usage habits, server-side architecture, service algorithm, service accuracy and service efficiency, so as to provide a theoretical and technical foundation for building a privacy-preserving platform for public health information services.

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

Similar content being viewed by others

Availability of data and materials

Please contact authors for data requests.

Notes

  1. International Classification of Diseases (ICD-9) - https://www.cdc.gov/nchs/icd/icd9.htm

References

  1. Zhou, X., Yu, X.: Value cognition and practice exploration of public library health service. J. Libr. Sci. China 48(4), 90–103 (2022)

    Google Scholar 

  2. Meng, Y., Dong, L., Guan, R., et al.: An analysis of university students’ health information service needs from academic library in the post-COVID-19 age through Kano model. Libr. Hi Tech 39(3), 711–721 (2021)

    Article  Google Scholar 

  3. Gao, C., Xu, J., Liu, Y., et al.: Nutrition policy and healthy China 2030 building. Eur. J. Clin. Nutr. 75(2), 238–246 (2021)

    Article  Google Scholar 

  4. Zhu, Q., Yang, M., Zhao, Y., et al.: Health information behavior: History, category and prospect. J. Libr. Sci. China 48(2), 94–107 (2022)

    Google Scholar 

  5. Hu, C., Hu, Q., Deng, S.: Information services and users-The, 4th edn. Wuhan University Press, China (2022)

    Google Scholar 

  6. Shen, Y., Shen, S., Li, Q., et al.: Evolutionary privacy-preserving learning strategies for edge-based IoT data sharing schemes. Digital Communications and Networks (2022)

  7. Wang, T., Bhuiyan, M., Wang, G., et al.: Preserving balance between privacy and data integrity in edge-assisted Internet of Things. IEEE Internet Things J. 7(4), 2679–2689 (2019)

    Article  Google Scholar 

  8. Nosouhi, M., Yu, S., Sood, K., et al.: Ucoin: An efficient privacy preserving scheme for cryptocurrencies. IEEE Trans. Dependable Secure Comput. 20(1), 242–255 (2023)

    Article  Google Scholar 

  9. Wu, Z., Xuan, S., Xie, J., et al.: How to ensure the confidentiality of electronic medical records on the cloud: A technical perspective. Comput. Biol. Med. 147, 105726 (2022)

    Article  Google Scholar 

  10. Thapa, C., Camtepe, S.: Precision health data: Requirements, challenges and existing techniques for data security and privacy. Comput. Biol. Med. 129, 104130 (2021)

    Article  Google Scholar 

  11. Li, T., Wang, H., He, D., et al.: Blockchain-based privacy-preserving and rewarding private data sharing for IoT. IEEE Internet Things J. 9(16), 15138–15149 (2022)

    Article  Google Scholar 

  12. Wu, Z., Shen, S., Li, H., et al.: A basic framework for privacy protection in personalized information retrieval. J. Organ. End User Comput. 33(6), 1–26 (2021)

    Article  Google Scholar 

  13. Wu, B., Chen, X., Mei, Z., et al.: Privacy-guarding optimal route finding with support for semantic search on encrypted graph in cloud computing scenario. Wirel. Commun. Mob. Comput. 2021, 1–12 (2021)

    Google Scholar 

  14. Wu, Z., Xie, J, Shen, S., et al.: A confusion method for the protection of user topic privacy in Chinese keyword–based book retrieval. ACM Trans. Asian Low–Resour. Lang. Inf. Process. 22(5):146 (2023)

  15. Shen, Y., Shen, S., Zhou, H., et al.: Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination. J. Inf. Secur. Appl. 66, 103140 (2022)

    Google Scholar 

  16. Li, T., Wang, H., He, D., et al.: Synchronized provable data possession based on blockchain for digital twin. IEEE Trans Inf Forensics Secur. 17, 472–485 (2022)

    Article  Google Scholar 

  17. Li, Q., Zhang, Q., Huang, H., et al.: Secure, efficient, and weighted access control for cloud-assisted industrial IoT. IEEE Internet Things J. 9(18), 16917–16927 (2022)

    Article  Google Scholar 

  18. Cheng, Z., Yue, D., Shen, S., et al.: Secure frequency control of hybrid power system under DoS attacks via Lie algebra. IEEE Trans. Inf. Forensics Secur. 17, 1172–1184 (2022)

    Article  Google Scholar 

  19. Zhou, H., Shen, S., Liu, J.: Malware propagation model in wireless sensor networks under attackCdefense confrontation. Comput. Commun. 162, 51–58 (2020)

    Article  Google Scholar 

  20. Wu, Z., Lu, C., Zhao, Y., et al.: The protection of user preference privacy in personalized information retrieval: Challenges and overviews. Libri 71(3), 227–237 (2021)

    Article  Google Scholar 

  21. Wu, Z., Li, R., Zhou, Z., et al.: A user sensitive subject protection approach for book search service. J. Assoc. Inf. Sci. Technol. 71(2), 183–195 (2020)

    Article  Google Scholar 

  22. Wu, Z., Shen, S., Lu, C., et al.: How to protect reader lending privacy under a cloud environment: A technical method. Libr. Hi Tech 40(6), 1746–1765 (2022)

    Article  Google Scholar 

  23. Wu, Z., Shen, S., Li, H., et al.: A comprehensive study to the protection of digital library readers’ privacy under an untrusted network environment. Libr. Hi Tech 40(6), 1930–1953 (2022)

    Article  Google Scholar 

  24. Ravi, N., Krishna, C., Koren, I.: Enhancing vehicular anonymity in ITS: A new scheme for mix zones and their placement. IEEE Trans. Veh. Technol. 68(11), 10372–10381 (2019)

    Article  Google Scholar 

  25. Wu, Z., Wang, R., Li, Q., et al.: A location privacy-preserving system based on query range cover-up or location-based services. IEEE Trans. Veh. Technol. 69(5), 5244–5254 (2020)

    Article  Google Scholar 

  26. Boualouache, A., Senouci, S., Moussaoui, S.: PRIVANET: An efficient pseudonym changing and management framework for vehicular ad-hoc networks. IEEE Trans. Intell. Transp. Syst. 21(8), 3209–3218 (2019)

    Article  Google Scholar 

  27. Wu, Z., Shen, S., Lian, X., et al.: A dummy-based user privacy protection approach for text information retrieval. Knowledge-Based Syst. 195, 105679 (2020)

    Article  Google Scholar 

  28. Li, Y., Chang, Y., Cheng, M., et al.: Multi-value private information retrieval with colluding databases via trace functions. Inf. Sci. 543(3), 426–436 (2020)

    MathSciNet  Google Scholar 

  29. Mei, Z., Zhu, H., Cui, Z., et al.: Executing multi-dimensional range query efficiently and flexibly over outsourced ciphertexts in the cloud. Inf. Sci. 432, 79–96 (2018)

    Article  Google Scholar 

  30. Xie, J., Xuan, S., You, W., et al.: An effective model of confidentiality management of digital archives in a cloud environment. Electronics 11(18), 2831 (2022)

    Article  Google Scholar 

  31. Wu, Z., Xie, J., Lian, X., et al.: A privacy protection approach for XML-based archives management in a cloud environment. Electron. Libr. 37(6), 970–983 (2019)

    Article  Google Scholar 

  32. Xu, Z., Yang, T., Najafi, M.: Method of cumulative anomaly identification for security database based on discrete markov chain. Secur. Commun. Netw. 2022, 5113725 (2022)

    Google Scholar 

  33. Wu, Z., Zou, D., Shen, S., et al.: An effective approach for the protection of user commodity viewing privacy in e-commerce website. Knowl. Based Syst. 220, 106952 (2021)

    Article  Google Scholar 

  34. Rodriguez-Garcia, M., Batet, M., Snchez, D., et al.: Privacy protection of user profiles in online search via semantic randomization. Knowl. Inf. Syst. 63, 2455–2477 (2021)

    Article  Google Scholar 

  35. Wu, Z., Li, G., Liu, Q., et al.: Covering the sensitive subjects to protect personal privacy in personalized recommendation. IEEE Trans. Serv. Comput. 11(3), 493–506 (2018)

    Article  Google Scholar 

  36. Wu, Z., Xie, J., Pan, J., et al.: An effective approach for the protection of user privacy in a digital library. Libri 69(4), 315–324 (2019)

    Article  Google Scholar 

  37. Liu, H., Xu, X., Li, E., et al.: Anomaly detection with representative neighbors[J]. IEEE Trans. Neural Netw. Learn. Syst. 34(6), 2831–2841 (2023)

    Article  Google Scholar 

  38. Wu, Z., Li, G., Shen, S., et al.: Constructing dummy query sequences to protect location privacy and query privacy in location-based services. World Wide Web 24, 25–49 (2021)

    Article  Google Scholar 

  39. Jiang, H., Li, J., Zhao, P., et al.: Location privacy-preserving mechanisms in location-based services: A comprehensive survey. ACM Comput. Surv. 54(1), 1–36 (2021)

    Google Scholar 

  40. Shaham, S., Ding, M., Liu, B., et al.: Privacy preservation in location-based services: A novel metric and attack model. IEEE Trans. Mob. Comput. 20(10), 3006–3019 (2020)

    Article  Google Scholar 

  41. Zhang, S., Mao, X., Choo, K., et al.: A trajectory privacy-preserving scheme based on a dual-K mechanism for continuous location-based services. Inf. Sci. 527, 406–419 (2020)

    Article  Google Scholar 

  42. Jia, X., Luo, M., Wang, H., et al.: A blockchain-assisted privacy-aware authentication scheme for internet of medical things[J]. IEEE Internet Things J. 9(21), 21838–21850 (2022)

    Article  Google Scholar 

  43. Shen, L.: Blockchain technology for management of intangible cultural heritage. Sci. Program. 2021, 1–7 (2021)

    Google Scholar 

  44. Huo, R., Zeng, S., Wang, Z., et al.: A comprehensive survey on blockchain in industrial internet of things: Motivations, research progresses, and future challenges. IEEE Commun. Surv. Tutorials 24(1), 88–122 (2022)

    Article  Google Scholar 

  45. Centobelli, P., Cerchione, R., Del Vecchio, P., et al.: Blockchain technology for bridging trust, traceability and transparency in circular supply chain. Inf. Manag. 59(7), 103508 (2022)

    Article  Google Scholar 

  46. Xiong, G., Yan, K., Zhou, X.: A distributed learning-based sentiment analysis methods with Web applications. World Wide Web 25(5), 1905–1922 (2022)

    Article  Google Scholar 

  47. Ma, X., Zhu, J., Lin, Z., et al.: A state-of-the-art survey on solving non-IID data in Federated Learning. Futur. Gener. Comput. Syst. 135, 244–258 (2022)

    Article  Google Scholar 

  48. Tang, Z., Hu, H., Xu, C.: A federated learning method for network intrusion detection. Concurrency and Computation: Practice and Experience 34(10), e6812 (2022)

    Article  Google Scholar 

  49. Wu, Z., Zheng, C., Xie, J., et al.: An approach for the protection of users’ book browsing preference privacy in a digital library. The Electronic Library 36(6), 1154–1166 (2018)

    Article  Google Scholar 

  50. Wang, Y., Su, Z., Zhang, N., et al.: A survey on metaverse: Fundamentals, security, and privacy. IEEE Commun. Surv. Tutorials 25(1), 319–352 (2023)

    Article  Google Scholar 

  51. Wu, Z., Zhu, H., Li, G., et al.: An efficient Wikipedia semantic matching approach to text document classification. Inf. Sci. 393, 15–28 (2017)

    Article  MathSciNet  Google Scholar 

  52. Wu, Z., Lei, L., Li, G., et al.: A topic modeling-based approach to novel document automatic summarization. Expert Syst. Appl. 84, 12–23 (2017)

    Article  Google Scholar 

  53. Liu, H., Zhou, W., Wu, Z., et al.: Refining codes for locality sensitive hashing. IEEE Transactions on Knowledge and Data Engineering (2023)

  54. Shen, Y., Shen, S., Sun, P., et al.: Signaling game-based availability assessment for edge computing-assisted IoT systems with malware dissemination. J. Inf. Secur. Appl. 66, 103140 (2022)

    Google Scholar 

  55. Zhao, C., Zhou, Y., Lai, X.: An integrated framework with evolutionary algorithm for multi-scenario multi-objective optimization problems. Inf. Sci. 600, 342–361 (2022)

    Article  Google Scholar 

  56. Ying, C., Ying, C., Ban, C.: A performance optimization strategy based on degree of parallelism and allocation fitness. EURASIP J. Wirel. Commun. Network. 2018, 1–8 (2018)

    Google Scholar 

  57. Nguyen, B., Nguyen, T., Vu, Q., et al.: A novel nature-inspired algorithm for optimal task scheduling in fog-cloud blockchain system. IEEE Internet Things J. (2023)

  58. Shen, S., Wu, X., Sun, P., et al.: Optimal privacy preservation strategies with signaling Q-learning for edge-computing-based IoT resource grant systems. Expert Syst. Appl. 225, 120192 (2023)

    Article  Google Scholar 

  59. Zhao, L., Lin, T., Zhang, D., et al.: An ultra-low complexity and high efficiency approach for lossless alpha channel coding. IEEE Trans. Multimed. 22(3), 786–794 (2019)

    Article  Google Scholar 

  60. Zhang, S., Ren, W., Tan, X., et al.: Semantic-aware dehazing network with adaptive feature fusion. IEEE Tran. Cybern. 53(1), 454–467 (2021)

    Article  Google Scholar 

Download references

Funding

The work is supported by Zhejiang Philosophy and Social Science Planning Project (No 24QNYC21ZD and 22ZJQN45YB), the key project of Humanities and Social Sciences in Colleges and Universities of Zhejiang Province (No 2021GH017) and Zhejiang Provincial Natural Science Foundation (No LR23F020001).

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Shaoxing University, Shaoxing, Zhejiang, China

    Zongda Wu, Huawen Liu & Jian Xie

  2. Faculty of Engineering and IT, University of Technology Sydney, Sydney, Australia

    Guandong Xu

  3. School of Information Management, Wuhan University, Wuhan, Hubei, China

    Gang Li

  4. Zhejiang Institute of Mechanical and Electrical Engineering, Hangzhou, Zhejiang, China

    Chenglang Lu

Authors
  1. Zongda Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Huawen Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jian Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Guandong Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Gang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Chenglang Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Z. Wu and C. Lu wrote the main manuscript text, H. Liu and Z. Wu wrote the code and H. Liu and J. Xie prepared figures. All authors reviewed the manuscript.

Corresponding author

Correspondence to Chenglang Lu.

Ethics declarations

Ethical Approval

Not applicable.

Competing Interests

No, I declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

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

Wu, Z., Liu, H., Xie, J. et al. An effective method for the protection of user health topic privacy for health information services. World Wide Web 26, 3837–3859 (2023). https://doi.org/10.1007/s11280-023-01208-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-023-01208-5

Keywords

Search

Navigation