Skip to main content
SpringerLink
Log in

Quadratic Error Minimization in a Distributed Environment with Privacy Preserving

  • Conference paper
Privacy and Security Issues in Data Mining and Machine Learning (PSDML 2010)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 6549))

  • 1116 Accesses

Abstract

In this paper, we address the issue of privacy preserving data-mining. Specifically, we consider a scenario where each member j of T parties has its own private database. The party j builds a private classifier h j for predicting a binary class variable y. The aim of this paper consists in aggregating these classifiers h j in order to improve the individual predictions. Precisely, the parties wish to compute an efficient linear combinations over their classifier in a secure manner.

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

Access this chapter

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 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight 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

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Bartlett, P.L.: For valid generalization the size of the weights is more important than the size of the network. In: NIPS, pp. 134–140 (1996)

    Google Scholar 

  2. Boneh, D., Goh, E.-J., Nissim, K.: Evaluating 2-dnf formulas on ciphertexts. In: Kilian, J. (ed.) TCC 2005. LNCS, vol. 3378, pp. 325–341. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  3. Canetti, R.: Universally composable security: A new paradigm for cryptographic protocols. In: FOCS, pp. 136–145 (2001)

    Google Scholar 

  4. Cesa-Bianchi, N., Lugosi, G., Stoltz, G.: Minimizing regret with label efficient prediction. IEEE Transactions on Information Theory 51(6), 2152–2162 (2005)

    Article  MathSciNet  MATH  Google Scholar 

  5. Chen, T., Zhong, S.: Privacy-preserving backpropagation neural network learning. Trans. Neur. Netw. 20(10), 1554–1564 (2009)

    Article  Google Scholar 

  6. Cramer, R., Damgård, I., Nielsen, J.B.: Multiparty computation from threshold homomorphic encryption. In: Pfitzmann, B. (ed.) EUROCRYPT 2001. LNCS, vol. 2045, pp. 280–299. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  7. Damgård, I., Jurik, M.: A generalisation, a simplification and some applications of paillier’s probabilistic public-key system. In: Kim, K.-c. (ed.) PKC 2001. LNCS, vol. 1992, pp. 119–136. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  8. Elgamal, T.: A public key cryptosystem and a signature sheme based on discrete logarithms. IEEE Transactions on Information Theory 31, 469–472 (1985)

    Article  MathSciNet  MATH  Google Scholar 

  9. Fouque, P., Stern, J.: Fully distributed threshold rsa under standard assumptions. In: IACR Cryptology ePrint Archive: Report 2001/2008 (February 2001)

    Google Scholar 

  10. Gambs, S., Kégl, B., Aïmeur, E.: Privacy-preserving boosting. Data Min. Knowl. Discov. 14(1), 131–170 (2007)

    Article  MathSciNet  Google Scholar 

  11. Garay, J.A., Schoenmakers, B., Villegas, J.: Practical and secure solutions for integer comparison. In: Okamoto, T., Wang, X. (eds.) PKC 2007. LNCS, vol. 4450, pp. 330–342. Springer, Heidelberg (2007)

    Chapter  Google Scholar 

  12. Goldwasser, S., Micali, S., Rackoff, C.: The knowledge complexity of interactive proof-systems (extended abstract). In: STOC, pp. 291–304 (1985)

    Google Scholar 

  13. Han, S., Ng, W.K., Wan, L., Lee, V.C.S.: Privacy-preserving gradient-descent methods. IEEE Trans. Knowl. Data Eng. 22(6), 884–899 (2010)

    Article  Google Scholar 

  14. Guajardo, J., Mennink, B., Schoenmakers, B.: Modulo reduction for paillier encryptions and application to secure statistical analysis. In: Sion, R. (ed.) FC 2010. LNCS, vol. 6052, pp. 375–382. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  15. Lindell, Y., Pinkas, B.: Privacy preserving data mining. In: Bellare, M. (ed.) CRYPTO 2000. LNCS, vol. 1880, pp. 36–54. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  16. Goldreich, O., Michali, S., Wigderson, A.: How to play any mental game or a completeness theorem for protocols with honest majority. In: STOC, pp. 218–229 (1987)

    Google Scholar 

  17. 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)

    Chapter  Google Scholar 

  18. Ross Quinlan, J.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)

    Google Scholar 

  19. Schapire, R.E.: Theoretical views of boosting. In: Fischer, P., Simon, H.U. (eds.) EuroCOLT 1999. LNCS (LNAI), vol. 1572, pp. 1–10. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  20. Schapire, R.E., Freund, Y., Barlett, P., Lee, W.S.: Boosting the margin: A new explanation for the effectiveness of voting methods. In: ICML, pp. 322–330 (1997)

    Google Scholar 

  21. Schoenmakers, B., Tuyls, P.: Efficient binary conversion for paillier encrypted values. In: Vaudenay, S. (ed.) EUROCRYPT 2006. LNCS, vol. 4004, pp. 522–537. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  22. Vaidya, J., Clifton, C., Kantarcioglu, M., Scott Patterson, A.: Privacy-preserving decision trees over vertically partitioned data. TKDD 2(3) (2008)

    Google Scholar 

  23. Vapnik, V.: Principles of risk minimization for learning theory. In: NIPS, pp. 831–838 (1991)

    Google Scholar 

  24. Yu, H., Vaidya, J., Jiang, X.: Privacy-preserving svm classification on vertically partitioned data. In: Ng, W.-K., Kitsuregawa, M., Li, J., Chang, K. (eds.) PAKDD 2006. LNCS (LNAI), vol. 3918, pp. 647–656. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Laboratory ERIC, University of Lyon, France

    Gérald Gavin & Julien Velcin

Authors
  1. Gérald Gavin
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julien Velcin
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Johann Wolfgang Goethe University, Ruth-Moufang-Str. 1, 60438, Frankfurt am Main, Germany

    Christos Dimitrakakis

  2. Information Analytics Lab, IBM Research – Zurich, Säumerstrasse 4, 8803, Rüschlikon, Switzerland

    Aris Gkoulalas-Divanis

  3. Ecole Polytechnice Fédérale de Lausanne, I&C - ISC - LASEC, Bâtiment INF, Station 14, CH-1015, Lausanne, Switzerland

    Aikaterini Mitrokotsa

  4. Department of Computer and Communication Engineering, University of Thessaly, Glavani 37 & 28TH, GR 38221, Octovriou, Volos, Greece

    Vassilios S. Verykios

  5. Faculty of Engineering and Natural Sciences, Sabanci University, Orhanli, 34956, Tuzla, Istanbul, Turkey

    Yücel Saygin

Rights and permissions

Reprints and permissions

Copyright information

© 2011 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Gavin, G., Velcin, J. (2011). Quadratic Error Minimization in a Distributed Environment with Privacy Preserving. In: Dimitrakakis, C., Gkoulalas-Divanis, A., Mitrokotsa, A., Verykios, V.S., Saygin, Y. (eds) Privacy and Security Issues in Data Mining and Machine Learning. PSDML 2010. Lecture Notes in Computer Science(), vol 6549. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-19896-0_3

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-19896-0_3

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-19895-3

  • Online ISBN: 978-3-642-19896-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation