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Private-Key Fully Homomorphic Encryption for Private Classification

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Mathematical Software – ICMS 2018 (ICMS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10931))

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Abstract

Fully homomophic encryption enables private computation over sensitive data, such as medical data, via potentially quantum-safe primitives. In this extended abstract we provide an overview of an implementation of a private-key fully homomorphic encryption scheme in a protocol for private Naive Bayes classification. This protocol allows a data owner to privately classify her data point without direct access to the learned model. We implement this protocol by performing privacy-preserving classification of breast cancer data as benign or malignant.

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References

  1. The GNU MP Bignum Library. https://gmplib.org/

  2. Alperin-Sheriff, J., Peikert, C.: Faster bootstrapping with polynomial error. In: Garay, J.A., Gennaro, R. (eds.) CRYPTO 2014. LNCS, vol. 8616, pp. 297–314. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44371-2_17

    Chapter  Google Scholar 

  3. Ayday, E., Raisaro, J.L., Hengartner, U., Molyneaux, A., Hubaux, J.-P.: Privacy-preserving processing of raw genomic data. In: Garcia-Alfaro, J., Lioudakis, G., Cuppens-Boulahia, N., Foley, S., Fitzgerald, W.M. (eds.) DPM/SETOP -2013. LNCS, vol. 8247, pp. 133–147. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-642-54568-9_9

    Chapter  Google Scholar 

  4. Bos, J.W., Lauter, K., Loftus, J., Naehrig, M.: Improved security for a ring-based fully homomorphic encryption scheme. In: Stam, M. (ed.) IMACC 2013. LNCS, vol. 8308, pp. 45–64. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-45239-0_4

    Chapter  Google Scholar 

  5. Bost, R., Ada Popa, R., Tu, S., Goldwasser, S.: Machine learning classification over encrypted data. In: Symposium on Network and Distributed System Security (NDSS), February 2015

    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. Brakerski, Z., Vaikuntanathan, V.: Fully homomorphic encryption from ring-LWE and security for key dependent messages. In: Rogaway, P. (ed.) CRYPTO 2011. LNCS, vol. 6841, pp. 505–524. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-22792-9_29

    Chapter  Google Scholar 

  8. Brakerski, Z., Vaikuntanathan, V., Gentry, C.: Fully homomorphic encryption without bootstrapping. In: Innovations in Theoretical Computer Science (2012)

    Google Scholar 

  9. Bruekers, F., Katzenbeisser, S., Kursawe, K., Tuyls, P.: Privacy-preserving matching of DNA profiles. Technical report (2008)

    Google Scholar 

  10. Cheon, J.H., Kim, M., Lauter, K.: Homomorphic computation of edit distance. In: Brenner, M., Christin, N., Johnson, B., Rohloff, K. (eds.) FC 2015. LNCS, vol. 8976, pp. 194–212. Springer, Heidelberg (2015). https://doi.org/10.1007/978-3-662-48051-9_15

    Chapter  Google Scholar 

  11. 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 

  12. Gentry, C.: Fully homomorphic encryption using ideal lattices. In: Proceedings of the Forty-first Annual ACM Symposium on Theory of Computing, STOC 2009, pp. 169–178. ACM (2009)

    Google Scholar 

  13. Gentry, C., Halevi, S., Peikert, C., Smart, N.P.: Ring switching in BGV-style homomorphic encryption. In: Visconti, I., De Prisco, R. (eds.) SCN 2012. LNCS, vol. 7485, pp. 19–37. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-32928-9_2

    Chapter  Google Scholar 

  14. Gentry, C., Halevi, S., Smart, N.P.: Better bootstrapping in fully homomorphic encryption. In: Fischlin, M., Buchmann, J., Manulis, M. (eds.) PKC 2012. LNCS, vol. 7293, pp. 1–16. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-30057-8_1

    Chapter  Google Scholar 

  15. Gentry, C., Halevi, S., Smart, N.P.: Fully homomorphic encryption with polylog overhead. In: Pointcheval, D., Johansson, T. (eds.) EUROCRYPT 2012. LNCS, vol. 7237, pp. 465–482. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-29011-4_28

    Chapter  Google Scholar 

  16. Gentry, C., Sahai, A., Waters, B.: Homomorphic encryption from learning with errors: conceptually-simpler, asymptotically-faster, attribute-based. In: Canetti, R., Garay, J.A. (eds.) CRYPTO 2013. LNCS, vol. 8042, pp. 75–92. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-40041-4_5

    Chapter  Google Scholar 

  17. Gilad-Bachrach, R., Dowlin, N., Laine, K., Lauter, K., Naehrig, M., Wernsing, J.: CryptoNets: applying neural networks to encrypted data with high throughput and accuracy. In: PMLR, pp. 201–210, June 2016

    Google Scholar 

  18. Goldwasser, S., Micali, S.: Probabilistic encryption and how to play mental poker keeping secret all partial information. In: Proceedings of the Fourteenth Annual ACM Symposium on Theory of Computing, STOC 1982, pp. 365–377. ACM, New York (1982)

    Google Scholar 

  19. 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 

  20. Gribov, A., Kahrobaei, D., Shpilrain, V.: Private-key fully homomorphic encryption in rings. Groups, Complexity, Cryptology 10 (2018)

    Google Scholar 

  21. Halevi, S.: HElib: an implementation of homomorphic encryption (2013). https://github.com/shaih/HElib

  22. Kim, M., Lauter, K.: Private genome analysis through homomorphic encryption. Cryptology ePrint Archive, Report 2015/965 (2015). http://eprint.iacr.org/2015/965

  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. Lichman, M.: UCI machine learning repository (2013). http://archive.ics.uci.edu/ml

  25. Paillier, P.: Public-key cryptosystems based on composite degree residuosity classes. In: Stern, J. (ed.) EUROCRYPT 1999. LNCS, vol. 1592, pp. 223–238. Springer, Heidelberg (1999). https://doi.org/10.1007/3-540-48910-X_16

    Chapter  Google Scholar 

  26. Peikert, C.: A decade of lattice cryptography. Found. Trends Theor. Comput. Sci. 10(4), 283–424 (2016)

    Article  MathSciNet  Google Scholar 

  27. Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, CCS 2015, pp. 1310–1321. ACM, New York (2015)

    Google Scholar 

  28. Troncoso-Pastoriza, J.R., Katzenbeisser, S., Celik, M.: Privacy preserving error resilient DNA searching through oblivious automata. In: ACM Conference on Computer and Communications Security (CCS), pp. 519–528. ACM Press, Alexandria, October 29–Nov 2 2007 (2007)

    Google Scholar 

  29. Wolberg, W.H., Mangasarian, O.L.: Multisurface method of pattern separation for medical diagnosis applied to breast cytology. Proc. Nat. Acad. Sci. U.S.A 87, 9193–9196 (1990)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Computer Science, The Graduate Center, CUNY, New York, USA

    Alexander Wood & Delaram Kahrobaei

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, USA

    Alexander Wood & Kayvan Najarian

  3. Center for Integrative Research in Critical Care, University of Michigan, Ann Arbor, USA

    Alexander Wood & Kayvan Najarian

  4. Emergency Medicine Department, University of Michigan, Ann Arbor, USA

    Alexander Wood & Kayvan Najarian

  5. Department of Mathematics, The Graduate Center, CUNY, New York, USA

    Vladimir Shpilrain

  6. Department of Mathematics, The City College of New York, New York, USA

    Vladimir Shpilrain

  7. LifeNome Inc., New York, NY, USA

    Ali Mostashari

  8. Department of Computer Science, Tandon School of Engineering, New York University, New York, USA

    Delaram Kahrobaei

Authors
  1. Alexander Wood
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  2. Vladimir Shpilrain
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  3. Kayvan Najarian
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  4. Ali Mostashari
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  5. Delaram Kahrobaei
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Corresponding author

Correspondence to Alexander Wood .

Editor information

Editors and Affiliations

  1. University of Bath, Bath, United Kingdom

    James H. Davenport

  2. Johannes Kepler University, Linz, Austria

    Manuel Kauers

  3. University of Waterloo, Waterloo, Ontario, Canada

    George Labahn

  4. Czech Technical University in Prague, Prague 6, Czech Republic

    Josef Urban

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Wood, A., Shpilrain, V., Najarian, K., Mostashari, A., Kahrobaei, D. (2018). Private-Key Fully Homomorphic Encryption for Private Classification. In: Davenport, J., Kauers, M., Labahn, G., Urban, J. (eds) Mathematical Software – ICMS 2018. ICMS 2018. Lecture Notes in Computer Science(), vol 10931. Springer, Cham. https://doi.org/10.1007/978-3-319-96418-8_56

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  • DOI: https://doi.org/10.1007/978-3-319-96418-8_56

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96417-1

  • Online ISBN: 978-3-319-96418-8

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