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Privacy-preserving association rule mining based on electronic medical system

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Abstract

Privacy protection during collaborative distributed association rule mining is an important research, which has been widely used in market prediction, medical research and other fields. In medical research, Domadiya et al. (Sadhana 43(8):127, 2018) focused on mining association rules from horizontally distributed healthcare data to diagnose heart disease. They claimed they proposed a more effective privacy-preserving distributed association rule mining (PPDARM) scheme. However, a serious security scrutiny of the scheme is performed, and we find it vulnerable to protect the support of the itemsets from any electronic health record (EHR) system, which is the most important parameter Domadiya et al. tried to protect. In this paper, we first present the cryptanalysis of the PPDARM scheme proposed by Domadiya et al. as well as some revised performance analyses. Then a new PPDARM scheme with less interactions is proposed to avert the shortcomings of Domadiya et al., using the homomorphic properties of the distributed Paillier cryptosystem to accomplish the cooperative computation. Our scheme allows the directed authority (miner) to obtain the final results rather than all cooperative EHR systems, in case of semi-honest but pseudo EHR systems. Moreover, security analysis and performance evaluation demonstrate our proposal is efficient and feasible.

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Notes

  1. The symbol \(O(\cdot )\) is commonly used asymptotic complexity notations. We denote an asymptotic upper bound with \(O(\cdot )\).

  2. In our setting, we think the EHR systems may forge the support to obtain the final sum of the supports. Hence we make such an assumption in order to make the weakness in [19] not affect our scheme.

References

  1. Azees, M., Vijayakumar, P., Karuppiah, M., & Nayyar, A. (2021). An efficient anonymous authentication and confidentiality preservation schemes for secure communications in wireless body area networks. Wireless Networks, 27(3), 2119–2130.

    Article  Google Scholar 

  2. Bhatia, S., C, P., & Dey, N. (2020). Data mining and information retrieval. Opinion Mining. Information Retrieval

  3. Zhang, L., Wang, W., & Zhang, Y. (2019). Privacy preserving association rule mining: Taxonomy, techniques, and metrics. IEEE Access, 7, 45032–45047.

    Article  Google Scholar 

  4. Thabtah, F. A. (2007). A review of associative classification mining. Knowledge Engineering Review, 22(1), 37–65.

    Article  Google Scholar 

  5. Jain, A. K., Murty, M. N., & Flynn, P. J. (1999). Data clustering: A review. ACM Computing Surveys, 31(3), 264–323.

    Article  Google Scholar 

  6. Deogun, J. S., & Jiang, L. (2005). Prediction mining—an approach to mining association rules for prediction. In: D. Slezak, J. Yao, J. F. Peters, W. Ziarko, X. Hu (Eds.), Rough sets, fuzzy sets, data mining, and granular computing, 10th international conference, RSFDGrC 2005, Regina, Canada, August 31–September 3, 2005, Proceedings, Part II, Lecture Notes in Computer Science (Vol. 3642, pp. 98–108). Springer.

  7. Ma, C., Wang, B., Jooste, K., Zhang, Z., & Ping, Y. (2020). Practical privacy-preserving frequent itemset mining on supermarket transactions. IEEE Systems Journal, 14(2), 1992–2002.

    Article  Google Scholar 

  8. Ordonez, C. (2006). Association rule discovery with the train and test approach for heart disease prediction. IEEE Transactions on Information Technology in Biomedicine, 10(2), 334–343.

    Article  Google Scholar 

  9. Shin A. M., Lee I. H., & G. H. L. E. A. (2010). Diagnostic analysis of patients with essential hypertension using association rule mining. Healthcare Informatics Research,16(2), 77–81.

  10. Agrawal, R., & Srikant, R. (1994). Fast algorithms for mining association rules in large databases. In: J. B. Bocca, M. Jarke, C. Zaniolo (Eds.), VLDB’94, Proceedings of 20th international conference on very large data bases, September 12–15, 1994, Santiago de Chile, Chile (pp. 487–499). Morgan Kaufmann.

  11. Nahar, J., Imam, T., Tickle, K. S., & Chen, Y. P. (2013). Association rule mining to detect factors which contribute to heart disease in males and females. Expert Systems with Applications, 40(4), 1086–1093.

    Article  Google Scholar 

  12. Palaniappan, S., & Awang, R. (2008). Intelligent heart disease prediction system using data mining techniques. In: The 6th ACS/IEEE international conference on computer systems and applications, AICCSA 2008, Doha, Qatar, March 31-April 4, 2008 (pp. 108–115). IEEE Computer Society.

  13. Qamar, N., Yang, Y., Nádas, A., & Liu, Z. (2016). Querying medical datasets while preserving privacy. In: E. M. Shakshuki (Ed.), The 7th international conference on emerging ubiquitous systems and pervasive networks (EUSPN 2016)/The 6th international conference on current and future trends of information and communication technologies in healthcare (ICTH-2016)/affiliated workshops, September 19–22, 2016, London, Procedia Computer Science (Vol. 98, pp. 324–331). Elsevier.

  14. Liu, X., Deng, R. H., Yang, Y., Tran, N. H., & Zhong, S. (2018). Hybrid privacy-preserving clinical decision support system in fog-cloud computing. Future Generation Computer Systems, 78, 825–837.

    Article  Google Scholar 

  15. Baroni, L., Salles, R., & S.S.E.A. (2020). An analysis of malaria in the Brazilian legal amazon using divergent association rules. Journal of Biomedical Informatics, 108, 103512.

  16. Bostrom, A. C., Schafer, P., & K. D. E. A. (2006). Electronic health record. Cin Computers Informatics. Nursing,24(1), 44–52.

  17. Jensen, P. B., & Brunak, L. J. J. (2012). Mining electronic health records: Towards better research applications and clinical care. Nature Reviews Genetics, 13, 395–405.

    Article  Google Scholar 

  18. Gkoulalas-Divanis, A., Loukides, G., & Sun, J. (2014). Publishing data from electronic health records while preserving privacy: A survey of algorithms. Journal of Biomedical Informatics, 50, 4–19.

    Article  Google Scholar 

  19. Domadiya, N., & Rao, U. P. (2018). Privacy-preserving association rule mining for horizontally partitioned healthcare data: a case study on the heart diseases. Sadhana, 43(8), 127.

    Article  MathSciNet  Google Scholar 

  20. Nikunj Domadiya, U. P. R. (2019). Privacy preserving distributed association rule mining approach on vertically partitioned healthcare data. Procedia Computer Science, 148, 303–312.

    Article  Google Scholar 

  21. Yigzaw, K. Y., Budrionis, A., Marco-Ruiz, L., Henriksen, T. D., Halvorsen, P. A., & Bellika, J. G. (2020). Privacy-preserving architecture for providing feedback to clinicians on their clinical performance. BMC Medical Informatics Decision Making, 20(1), 116.

    Article  Google Scholar 

  22. Nanavati, N. R., & P.L., Jinwala, D.C. (2014). Analysis and evaluation of schemes for secure sum in collaborative frequent itemset mining across horizontally partitioned data. The. Journal of Engineering, 2014, 1–10.

    Article  Google Scholar 

  23. Diffie, W., & Hellman, M. E. (1976). New directions in cryptography. IEEE Transactions on Information Theory, 22(6), 644–654.

    Article  MathSciNet  Google Scholar 

  24. Paillier, P. (1999). Public-key cryptosystems based on composite degree residuosity classes. In: J. Stern (Ed.), Advances in cryptology-EUROCRYPT ’99, international conference on the theory and application of cryptographic techniques, Prague, Czech Republic, May 2–6, 1999, proceeding, Lecture Notes in Computer Science (Vol. 1592, pp. 223–238). Springer

  25. Shoup, V. (2017). The number theory library (ntl). http://www.shoup.net

  26. Cleveland heart disease data details (2016). http://archive.ics.uci.edu/ml/machine-learning-databases/heart-disease/heart-disease.names

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Acknowledgements

This research was funded by the National Key R&D Program of China under Grant No. 2017YFB0802000, the National Natural Science Foundation of China under Grant Nos. U19B2021, 61972457, the National Cryptography Development Fund under Grant No. MMJJ20180111, and Key Research and Development Program of Shaanxi under Grant No. 2020ZDLGY08-04.

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Authors and Affiliations

  1. State Key Laboratory of Integrated Service Networks, Xidian University, Xi’an, 710071, People’s Republic of China

    Wenju Xu, Qingqing Zhao, Yu Zhan, Baocang Wang & Yupu Hu

  2. Cryptographic Research Center, Xidian University, Xi’an, 710071, People’s Republic of China

    Baocang Wang

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  1. Wenju Xu
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  2. Qingqing Zhao
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  3. Yu Zhan
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Correspondence to Baocang Wang.

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Xu, W., Zhao, Q., Zhan, Y. et al. Privacy-preserving association rule mining based on electronic medical system. Wireless Netw 28, 303–317 (2022). https://doi.org/10.1007/s11276-021-02846-1

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