Peer-to-Peer Networking and Applications

, Volume 11, Issue 2, pp 334–347 | Cite as

Efficient privacy-preserving online medical primary diagnosis scheme on naive bayesian classification

  • Xiaoxia Liu
  • Hui ZhuEmail author
  • Rongxing Lu
  • Hui Li


With the advances of machine learning algorithms and the pervasiveness of network terminals, online medical primary diagnosis scheme, which can provide the primary diagnosis service anywhere anytime, has attracted considerable interest recently. However, the flourish of online medical primary diagnosis scheme still faces many challenges including information security and privacy preservation. In this paper, we propose an efficient and privacy-preserving medical primary diagnosis scheme, called PDiag, on naive Bayes classification. With PDiag, the sensitive personal health information can be processed without privacy disclosure during online medical primary diagnosis service. Specifically, based on an improved expression for the naive Bayes classifier, an efficient and privacy-preserving classification scheme is introduced with lightweight polynomial aggregation technique. The encrypted user query is directly operated at the service provider without decryption, and the diagnosis result can only be decrypted by user. Through extensive analysis, we show that PDiag ensures users’ health information and service provider’s prediction model are kept confidential, and has significantly less computation and communication overhead than existing schemes. In addition, performance evaluations via implementing PDiag on smartphone and computer demonstrate PDiag’s effectiveness in term of real environment.


Online medical primary diagnosis Privacy-preserving Naive Bayes classifier Polynomial aggregation 



This work was financially supported by the National Natural Science Foundation of China under Grant 61303218, Grant 6167241 and Grant U1401251, National Key Research and Development Program of China under Grant 2016YFB0800804, Natural Science Basic Research Plan in Shaanxi Province of China under Grant 2016JM6007, Research Foundations for the Central Universities of China under Grant JB161507, and China 111 Project under Grant B16037. We would like to thank the anonymous reviewers for their insightful comments and suggestions.


  1. 1.
    Mattio R (2014) Shortest average wait time for doctors in major cities increased one minute year over year.
  2. 2.
    news B (2016) Waiting lists: Increase in number for ni outpatient appointments. [Online]. Available:
  3. 3.
    Messenger S (2016) Breast cancer patient waits in wales shocking. [Online]. Available:
  4. 4.
    Chenguang H, Xiaomao F, Ye L (2013) Toward ubiquitous healthcare services with a novel efficient cloud platform. IEEE transactions on bio-medical engineering 60(1):230–234CrossRefGoogle Scholar
  5. 5.
    Anderson MP, Dubnicka SR (2014) A sequential naïve bayes classifier for dna barcodes. Stat Appl Genet Mol Biol 13(4):423–434MathSciNetCrossRefzbMATHGoogle Scholar
  6. 6.
    Bellazzi R, Zupan B (2008) Predictive data mining in clinical medicine: Current issues and guidelines. Int J Med Inform 77(2):81–97CrossRefGoogle Scholar
  7. 7.
    Blanco R, Inza I, Merino M, Quiroga J, Larrañaga P (2005) Feature selection in bayesian classifiers for the prognosis of survival of cirrhotic patients treated with tips. J Biomed Inform 38(5):376–388CrossRefGoogle Scholar
  8. 8.
    Ko EJ, Lee HJ, Lee JW (2007) Ontology-based context modeling and reasoning for u-healthcare. IEICE Trans Inf Syst 90(8):1262–1270CrossRefGoogle Scholar
  9. 9.
    Lu R, Lin X, Shen X (2013) Spoc: A secure and privacy-preserving opportunistic computing framework for mobile-healthcare emergency. IEEE Trans Parallel Distrib Syst 24(3):614–624CrossRefGoogle Scholar
  10. 10.
    Zhu H, Lu R, Huang C, Chen L, Li H (2015) An efficient privacy-preserving location based services query scheme in outsourced cloud. IEEE Trans Veh Technol PP(99):1–1. [Online]. Available: Google Scholar
  11. 11.
    Lu R, Zhu H, Liu X, Liu J, Shao J (2014) Toward efficient and privacy-preserving computing in big data era. IEEE Netw 28(4):46–50CrossRefGoogle Scholar
  12. 12.
    Bos JW, Lauter K, Naehrig M (2014) Private predictive analysis on encrypted medical data. J Biomed Inform 50:234–243CrossRefGoogle Scholar
  13. 13.
    Liu X, Lu R, Ma J, Chen L, Qin B (2015) Privacy-preserving patient-centric clinical decision support system on naive bayesian classification. IEEE Journal of Biomedical and Health Informatics 99:1–1Google Scholar
  14. 14.
    Rahulamathavan Y, Veluru S, Phan R-W, Chambers J, Rajarajan M (2014) Privacy-preserving clinical decision support system using gaussian kernel-based classification. IEEE Journal of Biomedical and Health Informatics 18(1):56–66CrossRefGoogle Scholar
  15. 15.
    Boneh D, Franklin M K (2001) Identity-based encryption from the weil pairing. In: Proceedings of the 21st Annual International Cryptology Conference on Advances in Cryptology, ser CRYPTO ’01. Springer-Verlag, London, UK, pp 213–229Google Scholar
  16. 16.
    Leung K M (2007) Naive bayesian classifier, Polytechnic University Department of Computer Science/Finance and Risk EngineeringGoogle Scholar
  17. 17.
    Ren J, Lee SD, Chen X, Kao B, Cheng R, Cheung D (2009) Naive bayes classification of uncertain data. In: Data Mining, 2009. ICDM’09 Ninth IEEE International Conference on. IEEE, pp 944–949Google Scholar
  18. 18.
    Rahulamathavan Y, Rajarajan M (2015) Efficient privacy-preserving facial expression classification. IEEE Trans Dependable Secure Comput 7516:1CrossRefGoogle Scholar
  19. 19.
    Boneh D, Shacham H (2001) Short signatures from the weil pairing. In: Advances in Cryptology 2001. Springer, pp 514– 532Google Scholar
  20. 20.
    Wolberg DWH (1995) UCI machine learning repository. [Online]. Available:
  21. 21.
    To GB, Brown G, To GB, Brown G (2004) Diversity in neural network ensembles. University of BirminghamGoogle Scholar
  22. 22.
    Zhou X, Wang S, Xu W, Ji G, Phillips P, Sun P, Zhang Y (2015) Detection of pathological brain in mri scanning based on wavelet-entropy and naive bayes classifier. In: Bioinformatics and biomedical engineering. Springer, pp 201–209Google Scholar
  23. 23.
    Güler I, Beyli EDÜ (2007) Multiclass support vector machines for eeg-signals classification. IEEE Trans Inf Technol Biomed 11(2):117–126CrossRefGoogle Scholar
  24. 24.
    Ajemba P, Ramirez L, Durdle N, Hill D, Raso V (2005) A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis. IEEE Trans Inf Technol Biomed 9(2):276–282CrossRefGoogle Scholar
  25. 25.
    Wang W, Chen S, Brune KA, Hruban RH, Parmigiani G, Klein AP (2007) Pancpro: risk assessment for individuals with a family history of pancreatic cancer. J Clin Oncol 25(11):1417–1422CrossRefGoogle Scholar
  26. 26.
    Barakat MNH, Bradley AP (2010) Intelligible support vector machines for diagnosis of diabetes mellitus. IEEE Trans Inf Technol Biomed 14(4):1114–1120CrossRefGoogle Scholar
  27. 27.
    Huang C-L, Liao H-C, Chen M-C (2008) Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Systems with Applications 34(1):578–587CrossRefGoogle Scholar
  28. 28.
    Sundar NA, Latha PP, Chandra MR (2012) Performance analysis of classification data mining techniques over heart disease database. IJESAT] International Journal of engineering science & advanced technology ISSN:2250–3676Google Scholar
  29. 29.
    Pattekari SA, Parveen A (2012) Prediction system for heart disease using naïve bayes. International Journal of Advanced Computer and Mathematical Sciences 3(3):290–294Google Scholar
  30. 30.
    Medhekar DS, Bote MP, Deshmukh SD (2013) Heart disease prediction system using naive bayes. Int J Enhanced Res Sci Technol Eng 3:2Google Scholar
  31. 31.
    Mathew G, Obradovic Z (2011) A privacy-preserving framework for distributed clinical decision support. In: Computational Advances in Bio and Medical Sciences (ICCABS), 2011 IEEE 1st International Conference on IEEE, pp 129–134Google Scholar
  32. 32.
    Kantarcıoglu M, Vaidya J, Clifton C (2003) Privacy preserving naive bayes classifier for horizontally partitioned data. In: IEEE ICDM workshop on privacy preserving data mining, pp 3–9Google Scholar
  33. 33.
    Yang Z, Zhong S, Wright RN (2005) Privacy-preserving classification of customer data without loss of accuracy. In: SDM. SIAM, pp 92–102Google Scholar
  34. 34.
    Yi X, Zhang Y (2009) Privacy-preserving naive bayes classification on distributed data via semi-trusted mixers. Inf Syst 34(3):371–380CrossRefGoogle Scholar
  35. 35.
    Sumana M, Hareesha KS (2014) Privacy preserving naive bayes classifier for horizontally partitioned data using secure division. International Journal of Network Security and Its Applications 6:6Google Scholar
  36. 36.
    Gangrade A, Patel R (2012) Privacy preserving naïve bayes classifier for horizontally distribution scenario using un-trusted third party. IOSR Journal of Computer Engineering (IOSRJCE) ISSN:2278–0661Google Scholar
  37. 37.
    Vaidya J, Clifton C (2004) Privacy preserving naïve bayes classifier for vertically partitioned data. In: SDM. SIAM, pp 522–526Google Scholar
  38. 38.
    Toshniwal D (2011) Privacy preserving naïve bayes classification using trusted third party computation over distributed progressive databases. Advances in Computer Science and Information Technology:24–32Google Scholar
  39. 39.
    Huai M, Huang L, Yang W, Li L, Qi M (2015) Privacy-Preserving Naive Bayes Classification. Springer International PublishingGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.State Key Laboratory of Integrated Services NetworksXidian UniversityXi’anChina
  2. 2.School of Electrical and Electronic EngineeringNanyang Technological UniversityNanyangSingapore

Personalised recommendations