Skip to main content
SpringerLink
Log in
Book cover

The International Conference on Healthcare Science and Engineering

ICHSE 2018: Proceedings of the 2nd International Conference on Healthcare Science and Engineering pp 175–187Cite as

Injecting Differential Privacy in Rules Extraction of Rough Set

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Electrical Engineering ((LNEE,volume 536))

Abstract

Data mining plays a very important role in various database applications. Medical data mining has been a popular data mining application with a vital role in improving the quality of medical services and promoting the development of the medical industry. There has been extensive research in rough set theory (RST) to mine potential patterns in medical data, which has important implications for clinical decision support and online medical diagnosis. Although medical data mining is very promising, the rapid development of this field still faces many challenges, such as information security and privacy issues. Under the assumption that data miners cannot be trusted, this paper combines the differential privacy and rough set rules’ extraction for the first time and proposes a new method to mine hidden patterns in medical data and ensure patient privacy. This algorithm uses the Laplacian mechanism to add noise to the credibility in the process of data mining while maximizing the utility of the data. Experiments show that our algorithm can effectively preserve the accuracy of data while protecting patient privacy.

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

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. E.S. Berner, Clinical Decision Support Systems, vol. 233 (Springer Science + Business Media, LLC, New York, 2007)

    Book  Google Scholar 

  2. M.A. Musen, B. Middleton, R.A. Greenes, Clinical decision-support systems, in Biomedical Informatics (Springer, London, 2014), pp. 643–674

    Google Scholar 

  3. H. Monkaresi, R.A. Calvo, H. Yan, A machine learning approach to improve contactless heart rate monitoring using a webcam. IEEE J. Biomed. Health Inf. 18(4), 1153–1160 (2014)

    Article  Google Scholar 

  4. V. Chaitali, B. Nikita, M. Darshana, A survey on various classification techniques for clinical decision support system. Int. J. Comput. Appl. 116(23), 14–17 (2015)

    Google Scholar 

  5. S. Kumar, H. Inbarani, Optimistic multi-granulation rough set based classification for medical diagnosis. Procedia Comput. Sci. 47, 374–382 (2015)

    Article  Google Scholar 

  6. H.H. Inbarani, A novel neighborhood rough set based classification approach for medical diagnosis. Procedia Comput. Sci. 47, 351–359 (2015)

    Article  Google Scholar 

  7. R. Ali, J. Hussain, M.H. Siddiqi et al., H2RM: a hybrid rough set reasoning model for prediction and management of diabetes mellitus. Sensors 15(7), 15921–15951 (2015)

    Article  Google Scholar 

  8. L. Sweeney, k-anonymity: a model for protecting privacy. Int. J. Uncertainty, Fuzziness Knowl. Based Syst. 10(05), 557–570 (2002)

    Article  MathSciNet  Google Scholar 

  9. A. Machanavajjhala, J. Gehrke, D. Kifer, L-diversity: privacy beyond k-anonymity, in Proceedings of the 22nd International Conference on Data Engineering, 2006. ICDE’06 (IEEE, 2006), pp. 24–24

    Google Scholar 

  10. N. Li, T. Li, S. Venkatasubramanian, T-closeness: privacy beyond k-anonymity and l-diversity, in IEEE 23rd International Conference on Data Engineering, 2007. ICDE 2007 (IEEE, 2007), pp. 106–115

    Google Scholar 

  11. M.B. Malik, M.A. Ghazi, R. Ali, Privacy preserving data mining techniques: current scenario and future prospects, in 2012 Third International Conference on Computer and Communication Technology (ICCCT) (IEEE, 2012), pp. 26–32

    Google Scholar 

  12. A. Gkoulalas-Divanis, G. Loukides, J. Sun, Publishing data from electronic health records while preserving privacy: a survey of algorithms. J. Biomed. Inform. 50, 4–19 (2014)

    Article  Google Scholar 

  13. C. Dwork, Differential privacy, in Encyclopedia of Cryptography and Security (Springer US, 2011), pp. 338–340

    Google Scholar 

  14. R.S. Ledley, L.B. Lusted, Reasoning foundations of medical diagnosis. Science 130(3366), 9–21 (1959)

    Article  Google Scholar 

  15. H.L. Chen, B. Yang, J. Liu et al., A support vector machine classifier with rough set-based feature selection for breast cancer diagnosis. Expert Syst. Appl. 38(7), 9014–9022 (2011)

    Article  Google Scholar 

  16. M. Kantarcıoglu, J. Vaidya, C. Clifton, Privacy preserving naive bayes classifier for horizontally partitioned data, in IEEE ICDM Workshop on Privacy Preserving Data Mining (2003), pp. 3–9

    Google Scholar 

  17. X. Liu, R. Lu, J. Ma, L. Chen, B. Qin, Privacy-preserving patient centric clinical decision support system on Naïve Bayesian classification. IEEE J. Biomed. Health Inf. 20(2), 655–668 (2016)

    Article  Google Scholar 

  18. M. Li, S. Yu, Y. Zheng, K. Ren, W. Lou, Scalable and secure sharing of personal health records in cloud computing using attribute-based encryption. IEEE Trans. Parallel Distrib. Syst. 24(1), 131–143 (2013)

    Article  Google Scholar 

  19. Y. Elmehdwi, B.K. Samanthula, W. Jiang, Secure k-nearest neighbor query over encrypted data in outsourced environments, in 2014 IEEE 30th International Conference on Data Engineering (ICDE) (IEEE, 2014), pp. 664–675

    Google Scholar 

  20. J.P. Hubaux, J. Fellay, E. Ayday et al., Privacy-preserving computation of disease risk by using genomic, clinical, and environmental data, in Proceedings of USENIX Security Workshop on Health Information Technologies (HealthTech” 13), no. EPFL-CONF-187118 (2013)

    Google Scholar 

  21. Y. Rahulamathavan, S. Veluru, R. Phan, J. Chambers, M. Rajarajan, Privacy-preserving clinical decision support system using gaussian kernel based classification. IEEE J. Biomed. Health Inf. 18(1), 56–66 (2014)

    Article  Google Scholar 

  22. C. Dwork, F. McSherry, K. Nissim, A. Smith, Calibrating noise to sensitivity in private data analysis, in Theory of Cryptography Conference (Springer, Berlin, Heidelberg, 2006), pp. 265–284

    Chapter  Google Scholar 

  23. F. McSherry, K. Talwar, Mechanism design via differential privacy, in 48th Annual IEEE Symposium on Foundations of Computer Science, 2007. FOCS’07 (IEEE, 2007), pp. 94–103

    Google Scholar 

  24. Z. Ding, Z. Qin, Z. Qin, Frequent symptom sets identification from uncertain medical data in differentially private way. Sci. Program. 2017, 1–10 (2017)

    MathSciNet  Google Scholar 

  25. N. Li, W. Qardaji, D. Su, J. Cao, PrivBasis: frequent itemset mining with differential privacy. Proc. VLDB Endowment 5(11), 1340–1351 (2012)

    Article  Google Scholar 

  26. R. Bhaskar, S. Laxman, A. Smith, A. Thakurta, Discovering frequent patterns in sensitive data, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2010), pp. 503–512

    Google Scholar 

  27. N. Li, W. Qardaji, D. Su, J. Cao, Privbasis: frequent itemset mining with differential privacy. Proc. VLDB Endowment 5(11), 1340–1351 (2012)

    Article  Google Scholar 

  28. X.J. Zhang, M. Wang, X.F. Meng, An accurate method for mining top-k frequent pattern under differential privacy. J. Comput. Res. Dev. 51(1), 104–114 (2014)

    Google Scholar 

  29. A. Blum, C. Dwork, F. McSherry, K. Nissim, Practical privacy: the SuLQ framework, in Proceedings of the Twenty-fourth ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems (ACM, 2005), pp. 128–138

    Google Scholar 

  30. A. Friedman, S. Assaf, Data mining with differential privacy, in Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (ACM, 2010), pp. 493–502

    Google Scholar 

  31. Q. Yu, Y. Luo, C. Chen, X. Ding, Outlier-eliminated k-means clustering algorithm based on differential privacy preservation. Appl. Intell. 45(4), 1179–1191 (2016)

    Article  Google Scholar 

  32. K. Chaudhuri, C. Monteleoni, Privacy-preserving logistic regression, in Advances in Neural Information Processing Systems (2009), pp. 289–296

    Google Scholar 

Download references

Acknowledgements

The research is supported by the National Science Foundation of China (Nos. 61672176, 61662008, 61502111), Guangxi “Bagui Scholar” Teams for Innovation and Research Project, the Guangxi Collaborative Center of Multi-source Information Integration and Intelligent Processing, Guangxi Natural Science Foundation (Nos. 2015GXNSFBA139246, 2016GXNSFAA380192), and the Innovation Project of Guangxi Graduate Education (Nos. YCSZ2015104, 2018KY0082).

Author information

Authors and Affiliations

  1. Guangxi Key Lab of Multi-Source Information Mining and Security, Guangxi Normal University, Guilin, 541004, China

    Xianxian Li, Chunfeng Luo, Peng Liu, Li-e Wang & Dongran Yu

Authors
  1. Xianxian Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chunfeng Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Peng Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li-e Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dongran Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to Li-e Wang or Dongran Yu .

Editor information

Editors and Affiliations

  1. Department of Computer Science, New Jersey Institute of Technology, Newark, NJ, USA

    Chase Q. Wu

  2. Department of Mechanical Engineering , Texas Tech University, Lubbock, TX, USA

    Ming-Chien Chyu

  3. Department of Communications, Universitat Politècnica de València, Valencia, Spain

    Jaime Lloret

  4. School of Computer Science, Guangxi Normal University, Guilin, Guangxi, China

    Xianxian Li

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Singapore Pte Ltd.

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Li, X., Luo, C., Liu, P., Wang, Le., Yu, D. (2019). Injecting Differential Privacy in Rules Extraction of Rough Set. In: Wu, C., Chyu, MC., Lloret, J., Li, X. (eds) Proceedings of the 2nd International Conference on Healthcare Science and Engineering . ICHSE 2018. Lecture Notes in Electrical Engineering, vol 536. Springer, Singapore. https://doi.org/10.1007/978-981-13-6837-0_13

Download citation

Publish with us

Policies and ethics

Search

Navigation