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International Conference on Bio-inspired Information and Communication

BICT 2019: Bio-inspired Information and Communication Technologies pp 98–111Cite as

Medical Diagnostics Based on Encrypted Medical Data

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Abstract

We utilize a type of encryption scheme known as a Fully Homomorphic Encryption (FHE) scheme which allows for computation over encrypted data. Our encryption scheme is more efficient than other publicly available FHE schemes, making it more feasible. We conduct simulations based on common scenarios in which this ability is useful. In the first simulation we conduct time series analysis via Recursive Least Squares on both encrypted and unencrypted data and compare the results. In simulation one, it is shown that the error from computing over plaintext data is the same as the error for computing over encrypted data. In the second simulation, we compute two known diagnostic functions over publicly available data in order to calculate computational benchmarks. In simulation two, we see that computation over encrypted data using our method incurs relatively lower costs as compared to a majority of other publicly available methods. By successfully computing over encrypted data we have shown that our FHE scheme permits the use of machine learning algorithms that utilize polynomial kernel functions.

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References

  1. Aono, Y., Hayashi, T., Trieu Phong, L., Wang, L.: Scalable and secure logistic regression via homomorphic encryption. In: Proceedings of the Sixth ACM Conference on Data and Application Security and Privacy, pp. 142–144. ACM (2016)

    Google Scholar 

  2. Aslett, L.J., Esperança, P.M., Holmes, C.C.: A review of homomorphic encryption and software tools for encrypted statistical machine learning. arXiv preprint arXiv:1508.06574 (2015)

  3. Bos, J.W., Lauter, K., Naehrig, M.: Private predictive analysis on encrypted medical data. J. Biomed. Inform. 50, 234–243 (2014). Special Issue on Informatics Methods in Medical Privacy

    Article  Google Scholar 

  4. Bost, R., Popa, R.A., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: NDSS (2015)

    Google Scholar 

  5. Brakerski, Z., Gentry, C., Vaikuntanathan, V.: (Leveled) fully homomorphic encryption without bootstrapping. ACM Trans. Computat. Theor. (TOCT) 6(3), 13 (2014)

    MathSciNet  MATH  Google Scholar 

  6. Brakerski, Z., Vaikuntanathan, V.: Efficient fully homomorphic encryption from (standard) LWE. In: Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science, FOCS 2011, pp. 97–106. IEEE Computer Society, Washington, DC (2011)

    Google Scholar 

  7. Centers for Disease Control and Prevention: HIPAA privacy rule and public health. Guidance from CDC and the US department of health and human services. MMWR Morb. Mortal. Wkly. Rep. 52(Suppl. 1), 1–17 (2003)

    Google Scholar 

  8. Chillotti, I., Gama, N., Georgieva, M., Izabachène, M.: Faster fully homomorphic encryption: bootstrapping in less than 0.1 seconds. In: Cheon, J.H., Takagi, T. (eds.) ASIACRYPT 2016. LNCS, vol. 10031, pp. 3–33. Springer, Heidelberg (2016). https://doi.org/10.1007/978-3-662-53887-6_1

    Chapter  MATH  Google Scholar 

  9. Du, W., Han, Y.S., Chen, S.: Privacy-preserving multivariate statistical analysis: linear regression and classification. In: Proceedings of the 2004 SIAM International Conference on Data Mining, pp. 222–233. SIAM (2004)

    Google Scholar 

  10. Ducas, L., Micciancio, D.: FHEW: bootstrapping homomorphic encryption in less than a second. In: Oswald, E., Fischlin, M. (eds.) EUROCRYPT 2015. LNCS, vol. 9056, pp. 617–640. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-46800-5_24

    Chapter  MATH  Google Scholar 

  11. El Emam, K., Jonker, E., Arbuckle, L., Malin, B.: A systematic review of re-identification attacks on health data. PloS One 6(12), e28071 (2011)

    Article  Google Scholar 

  12. Evans, D., Huang, Y., Katz, J., Malka, L.: Efficient privacy-preserving biometric identification. In: Proceedings of the 17th conference Network and Distributed System Security Symposium, NDSS (2011)

    Google Scholar 

  13. Fan, J., Vercauteren, F.: Somewhat practical fully homomorphic encryption. IACR Cryptology ePrint Archive 2012, 144 (2012)

    Google Scholar 

  14. Gentry, C., Boneh, D.: A Fully Homomorphic Encryption Scheme, vol. 20. Stanford University, Stanford (2009)

    Google Scholar 

  15. Gentry, C., Halevi, S.: Implementing Gentry’s fully-homomorphic encryption scheme. In: Paterson, K.G. (ed.) EUROCRYPT 2011. LNCS, vol. 6632, pp. 129–148. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-20465-4_9

    Chapter  Google Scholar 

  16. Graepel, T., Lauter, K., Naehrig, M.: ML confidential: machine learning on encrypted data. In: Kwon, T., Lee, M.-K., Kwon, D. (eds.) ICISC 2012. LNCS, vol. 7839, pp. 1–21. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-37682-5_1

    Chapter  Google Scholar 

  17. Gribov, A., Kahrobaei, D., Shpilrain, V.: Practical private-key fully homomorphic encryption in rings. Groups Complex. Cryptol. 10(1), 17–27 (2018)

    MathSciNet  MATH  Google Scholar 

  18. Grigoriev, D., Ponomarenko, I.: Homomorphic public-key cryptosystems over groups and rings. arXiv preprint cs/0309010 (2003)

    Google Scholar 

  19. Halevi, S., Shoup, V.: Algorithms in HElib. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8616, pp. 554–571. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44371-2_31

    Chapter  MATH  Google Scholar 

  20. Halevi, S., Shoup, V.: Helib (2014). HELib: https://github.com.shaih/HElib

  21. Hall, R., Fienberg, S.E., Nardi, Y.: Secure multiple linear regression based on homomorphic encryption. J. Off. Stat. 27(4), 669 (2011)

    Google Scholar 

  22. Kahrobaei, D., Lam, H., Shpilrain, V.: System and method for private-key fully homomorphic encryption and private search between rings. Patent US20170063526, 25 August 2017

    Google Scholar 

  23. Lauter, K., López-Alt, A., Naehrig, M.: Private computation on encrypted genomic data. In: Aranha, D.F., Menezes, A. (eds.) LATINCRYPT 2014. LNCS, vol. 8895, pp. 3–27. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-16295-9_1

    Chapter  Google Scholar 

  24. Lindell, P.: Privacy preserving data mining. J. Cryptol. 15(3), 177–206 (2002)

    Article  MathSciNet  Google Scholar 

  25. Liu, F., Ng, W.K., Zhang, W.: Encrypted SVM for outsourced data mining. In: 2015 IEEE 8th International Conference on Cloud Computing (CLOUD), pp. 1085–1092. IEEE (2015)

    Google Scholar 

  26. Ljung, L.: System Identification: Theory for the User. Prentice-Hall, Upper Saddle River (1987)

    MATH  Google Scholar 

  27. Naehrig, M., Lauter, K., Vaikuntanathan, V.: Can homomorphic encryption be practical? In: Proceedings of the 3rd ACM Workshop on Cloud Computing Security Workshop, pp. 113–124. ACM (2011)

    Google Scholar 

  28. Nikolaenko, V., Weinsberg, U., Ioannidis, S., Joye, M., Boneh, D., Taft, N.: Privacy-preserving ridge regression on hundreds of millions of records. In: 2013 IEEE Symposium on Security and Privacy, pp. 334–348, May 2013

    Google Scholar 

  29. Poggio, T., Smale, S.: The mathematics of learning: dealing with data. Not. AMS 50(5), 537–544 (2003)

    MathSciNet  MATH  Google Scholar 

  30. Rigney, D.R., Goldberger, A.L., Ocasio, W., Ichimaru, Y., Moody, G.B., Mark, R.: Multi-channel physiological data: description and analysis (1993)

    Google Scholar 

  31. Sadeghi, A.-R., Schneider, T., Wehrenberg, I.: Efficient privacy-preserving face recognition. In: Lee, D., Hong, S. (eds.) ICISC 2009. LNCS, vol. 5984, pp. 229–244. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14423-3_16

    Chapter  Google Scholar 

  32. Sarwate, A.D., Chaudhuri, K.: Signal processing and machine learning with differential privacy: algorithms and challenges for continuous data. IEEESignal Process. Mag. 30(5), 86–94 (2013)

    Article  Google Scholar 

  33. Strack, B., et al.: Impact of HbA1c measurement on hospital readmission rates: analysis of 70,000 clinical database patient records. BioMed Res. Int. 2014 (2014)

    Article  Google Scholar 

  34. Tsanas, A., Little, M.A., McSharry, P.E., Ramig, L.O.: Accurate telemonitoring of Parkinson’s disease progression by noninvasive speech tests. IEEE Trans. Biomed. Eng. 57(4), 884–893 (2010)

    Article  Google Scholar 

  35. van Dijk, M., Gentry, C., Halevi, S., Vaikuntanathan, V.: Fully homomorphic encryption over the integers. In: Gilbert, H. (ed.) EUROCRYPT 2010. LNCS, vol. 6110, pp. 24–43. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-13190-5_2

    Chapter  Google Scholar 

  36. Wiens, J., Guttag, J., Horvitz, E.: A study in transfer learning: leveraging data from multiple hospitals to enhance hospital-specific predictions. J. Am. Med. Inform. Assoc. 21(4), 699–706 (2014)

    Article  Google Scholar 

  37. Yang, J.J., Li, J.Q., Niu, Y.: A hybrid solution for privacy preserving medical data sharing in the cloud environment. Futur. Gener. Comput. Syst. 43, 74–86 (2015)

    Article  Google Scholar 

  38. Zhang, J., Zhang, Z., Xiao, X., Yang, Y., Winslett, M.: Functional mechanism: regression analysis under differential privacy. Proc. VLDB Endow. 5(11), 1364–1375 (2012)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. The Graduate Center, The City University of New York, 365 Fifth Ave, New York, NY, 10016, USA

    Alexey Gribov, Kelsey Horan, Vladimir Shpilrain & Delaram Kahrobaei

  2. Michigan Center for Integrative Research in Critical Care, University of Michigan Ann Arbor, 2800 Plymouth Road, Ann Arbor, MI, 48109, USA

    Kelsey Horan, Jonathan Gryak, Kayvan Najarian & Reza Soroushmehr

  3. University of York, Heslington, York, YO10 5DD, UK

    Delaram Kahrobaei

Authors
  1. Alexey Gribov
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  2. Kelsey Horan
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  3. Jonathan Gryak
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  4. Kayvan Najarian
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  5. Vladimir Shpilrain
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  6. Reza Soroushmehr
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  7. Delaram Kahrobaei
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Corresponding author

Correspondence to Kelsey Horan .

Editor information

Editors and Affiliations

  1. Stefens Institute of Technology USA, Hoboken, NJ, USA

    Adriana Compagnoni

  2. Carnegie Mellon University, Pittsburgh, PA, USA

    William Casey

  3. Cylab, Carnegie Mellon University, Pittsburgh, PA, USA

    Yang Cai

  4. New York University, New York, NY, USA

    Bud Mishra

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© 2019 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Gribov, A. et al. (2019). Medical Diagnostics Based on Encrypted Medical Data. In: Compagnoni, A., Casey, W., Cai, Y., Mishra, B. (eds) Bio-inspired Information and Communication Technologies. BICT 2019. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 289. Springer, Cham. https://doi.org/10.1007/978-3-030-24202-2_8

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  • DOI: https://doi.org/10.1007/978-3-030-24202-2_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-24201-5

  • Online ISBN: 978-3-030-24202-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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