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Differentially Private Multi-task Learning

  • Sunil Kumar GuptaEmail author
  • Santu Rana
  • Svetha Venkatesh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9650)

Abstract

Privacy restrictions of sensitive data repositories imply that the data analysis is performed in isolation at each data source. A prime example is the isolated nature of building prognosis models from hospital data and the associated challenge of dealing with small number of samples in risk classes (e.g. suicide) while doing so. Pooling knowledge from other hospitals, through multi-task learning, can alleviate this problem. However, if knowledge is to be shared unrestricted, privacy is breached. Addressing this, we propose a novel multi-task learning method that preserves privacy of data under the strong guarantees of differential privacy. Further, we develop a novel attribute-wise noise addition scheme that significantly lifts the utility of the proposed method. We demonstrate the effectiveness of our method with a synthetic and two real datasets.

Keywords

Cancer Dataset Privacy Preserve Privacy Requirement Task Parameter Differential Privacy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    Chin, F.Y., Ozsoyoglu, G.: Auditing and inference control in statistical databases. IEEE Trans. Softw. Eng. 8(6), 574–582 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  2. 2.
    LeFevre, K., DeWitt, D.J., Ramakrishnan, R.: Incognito: Efficientfull-domain k-anonymity. In: SIGMOD, pp. 49–60. ACM (2005)Google Scholar
  3. 3.
    Ben-David, A., Nisan, N., Pinkas, B.: Fairplaymp: a system for securemulti-party computation. In: ACM CCS, pp. 257–266. ACM (2008)Google Scholar
  4. 4.
    Traub, J.F., Yemini, Y., Woźniakowski, H.: The statistical security of a statistical database. TODS 9(4), 672–679 (1984)CrossRefGoogle Scholar
  5. 5.
    Dinur, I., Nissim, K.: Revealing information while preserving privacy. In: PODS, pp. 202–210. ACM (2003)Google Scholar
  6. 6.
    Ganta, S., Kasiviswanathan, S., Smith, A.: Composition attacks and auxiliary information in data privacy. In: SIGKDD, pp. 265–273. ACM (2008)Google Scholar
  7. 7.
    Dwork, C., McSherry, F., Nissim, K., Smith, A.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) TCC 2006. LNCS, vol. 3876, pp. 265–284. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  8. 8.
    Vaidya, J., Clifton, C.W., Zhu, Y.M.: Privacy Preserving Data Mining, vol. 19. Springer Science & Business Media, New York (2006)zbMATHGoogle Scholar
  9. 9.
    Chaudhuri, K., Monteleoni, C., Sarwate, A.D.: Differentially private empirical risk minimization. J. Mach. Learn. Res. 12, 1069–1109 (2011)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Argyriou, A., Evgeniou, T., Pontil, M.: Convex multi-task feature learning. Mach. Learn. 73(3), 243–272 (2008)CrossRefGoogle Scholar
  11. 11.
    Saha, B., Gupta, S., Phung, D., Venkatesh, S.: Multiple task transfer learning with small sample sizes. In: Knowledge and Information Systems, pp. 1–28 (2015)Google Scholar
  12. 12.
    Zhang, Y., Yeung, D.-Y.: A convex formulation for learning task relationships in multi-task learning. In: Uncertainty in Artificial Intelligence, pp. 733–442 (2010)Google Scholar
  13. 13.
    Mathew, G., Obradovic, Z.: Distributed privacy preserving decision support system for predicting hospitalization risk in hospitals with insufficient data. In: ICMLA, vol. 2, pp. 178–183 (2012)Google Scholar
  14. 14.
    Pathak, M., Rane, S., Raj, B.: Multiparty differential privacy via aggregation of locally trained classifiers. In: NIPS, pp. 1876–1884 (2010)Google Scholar
  15. 15.
    Spall, J.C.: Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control, vol. 65. Wiley, Hoboken (2005)zbMATHGoogle Scholar
  16. 16.
    Tran, T., Luo, W., Phung, D., Gupta, S., Rana, S., Kennedy, R.L., Larkins, A., Venkatesh, S.: A framework for feature extraction from hospital medical data with applications in risk prediction. BMC Bioinform. 15(1), 6596 (2014)CrossRefGoogle Scholar
  17. 17.
    Rana, S., Gupta, S., Venkatesh, S.: Differentially-private random forest with high utility. In: ICDM, pp. 955–960. IEEE, Atlantic City (2015)Google Scholar
  18. 18.
    Gupta, S., Rana, S., Saha, B., Phung, D., Venkatesh, S.: A new transfer learning framework with application to model-agnostic multi-task learning. In: KAIS (2015)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sunil Kumar Gupta
    • 1
    Email author
  • Santu Rana
    • 1
  • Svetha Venkatesh
    • 1
  1. 1.Center for Pattern Recognition and Data AnalyticsDeakin UniversityGeelongAustralia

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