Skip to main content
SpringerLink
Log in

Towards Privacy-Preserving Model Selection

  • Conference paper
Privacy, Security, and Trust in KDD (PInKDD 2007)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 4890))

Included in the following conference series:

Abstract

Model selection is an important problem in statistics, machine learning, and data mining. In this paper, we investigate the problem of enabling multiple parties to perform model selection on their distributed data in a privacy-preserving fashion without revealing their data to each other. We specifically study cross validation, a standard method of model selection, in the setting in which two parties hold a vertically partitioned database. For a specific kind of vertical partitioning, we show how the participants can carry out privacy-preserving cross validation in order to select among a number of candidate models without revealing their data to each other.

This work was supported in part by the National Science Foundation under Grant No. CCR-0331584 and by the Department of Homeland Security under ONR Grant N00014-07-1-0159.

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. Agrawal, D., Aggarwal, C.: On the design and quantification of privacy preserving data mining algorithms. In: Proc. of the 20th ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 247–255 (2001)

    Google Scholar 

  2. Agrawal, R., Evfimievski, A., Srikant, R.: Information sharing across private databases. In: Proc. of the 2003 ACM SIGMOD International Conference on Management of Data, pp. 86–97 (2003)

    Google Scholar 

  3. Agrawal, R., Srikant, R.: Privacy preserving data mining. In: Proc. of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 439–450 (May 2000)

    Google Scholar 

  4. Atallah, M., Du., W.: Secure multi-party computational geometry. In: Proc. of the Seventh International Workshop on Algorithms and Data Structures, pp. 165–179. Springer, Heidelberg (2001)

    Google Scholar 

  5. Boneh, D.: The decision Diffie-Hellman problem. In: Buhler, J.P. (ed.) ANTS 1998. LNCS, vol. 1423, pp. 48–63. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

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

    Google Scholar 

  7. Canetti, R., Ishai, Y., Kumar, R., Reiter, M., Rubinfeld, R., Wright, R.: Selective private function evaluation with applications to private statistics. In: Proc. of the 20th Annual ACM Symposium on Principles of Distributed Computing, pp. 293–304 (2001)

    Google Scholar 

  8. Du, W., Zhan, Z.: Using randomized response techniques for privacy-preserving data mining. In: Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 505–510 (2003)

    Google Scholar 

  9. ElGamal, T.: A public-key cryptosystem and a signature scheme based on discrete logarithms. IEEE Transactions on Information Theory IT-31(4) (1985)

    Google Scholar 

  10. Evfimievski, A., Gehrke, J., Srikant, R.: Limiting privacy breaches in privacy preserving data mining. In: Proc. of the 22nd ACM SIGMOD-SIGACT-SIGART Symposium on Principles of Database Systems, pp. 211–222 (2003)

    Google Scholar 

  11. Evfimievski, A., Srikant, R., Agrawal, R., Gehrke, J.: Privacy preserving mining of association rules. In: Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 217–228 (2002)

    Google Scholar 

  12. Feigenbaum, J., Ishai, Y., Malkin, T., Nissim, K., Strauss, M., Wright, R.: Secure multiparty computation of approximations. ACM Transactions on Algorithms 2(3), 435–472 (2005)

    Article  MathSciNet  Google Scholar 

  13. Freedman, M., Nissim, K., Pinkas, B.: Efficient private matching and set intersection. In: Cachin, C., Camenisch, J.L. (eds.) EUROCRYPT 2004. LNCS, vol. 3027, pp. 1–19. Springer, Heidelberg (2004)

    Google Scholar 

  14. Goethals, B., Laur, S., Lipmaa, H., Mielikäinen, T.: On private scalar product computation for privacy-preserving data mining. In: Park, C.-s., Chee, S. (eds.) ICISC 2004. LNCS, vol. 3506, Springer, Heidelberg (2005)

    Google Scholar 

  15. Goldreich, O.: Foundations of Cryptography, Volume II: Basic Applications. Cambridge University Press, Cambridge (2004)

    Google Scholar 

  16. Goldreich, O., Micali, S., Wigderson, A.: How to play ANY mental game. In: Proc. of the 19th Annual ACM Conference on Theory of Computing, pp. 218–229 (1987)

    Google Scholar 

  17. Huang, Z., Du, W., Chen, B.: Deriving private information from randomized data. In: Proceedings of the ACM SIGMOD Conference (2005)

    Google Scholar 

  18. Indyk, P., Woodruff, D.: Polylogarithmic private approximations and efficient matching. In: Prof. of the Third Theory of Cryptography Conference. LNCS, Springer, Heidelberg (2006)

    Google Scholar 

  19. Jagannathan, G., Wright, R.N.: Privacy-preserving distributed k-means clustering over arbitrarily partitioned data. In: Proc. of the 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 593–599 (2005)

    Google Scholar 

  20. Jagannathan, G., Wright, R.N.: Privacy-preserving data imputation. In: Proc. of the ICDM Int. Workshop on Privacy Aspects of Data Mining, pp. 535–540 (2006)

    Google Scholar 

  21. Kantarcioglu, M., Clifton, C.: Privacy-preserving distributed mining of association rules on horizontally partitioned data. In: Proc. of the ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 2002), pp. 24–31 (June 2002)

    Google Scholar 

  22. Kantarcioglu, M., Vaidya, J.: Privacy preserving naive Bayes classifier for horizontally partitioned data. In: IEEE Workshop on Privacy Preserving Data Mining (2003)

    Google Scholar 

  23. Kargupta, H., Datta, S., Wang, Q., Sivakumar, K.: On the privacy preserving properties of random data perturbation techniques. In: The Third IEEE International Conference on Data Mining (2003)

    Google Scholar 

  24. Laur, S., Lipmaa, H., Mielikäinen, T.: Cryptographically private support vector machines. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 618–624 (2006)

    Google Scholar 

  25. Lindell, Y., Pinkas, B.: Privacy preserving data mining. J. Cryptology 15(3), 177–206 (2002)

    Article  MATH  MathSciNet  Google Scholar 

  26. Liu, K., Kargupta, H., Ryan, J.: Multiplicative noise, random projection, and privacy preserving data mining from distributed multi-party data. Technical Report TR-CS-03-24, Computer Science and Electrical Engineering Department, University of Maryland, Baltimore County (2003)

    Google Scholar 

  27. Meng, D., Sivakumar, K., Kargupta, H.: Privacy-sensitive Bayesian network parameter learning. In: Proc. of the Fourth IEEE International Conference on Data Mining, Brighton, UK (2004)

    Google Scholar 

  28. Rizvi, S., Haritsa, J.: Maintaining data privacy in association rule mining. In: Proc. of the 28th VLDB Conference (2002)

    Google Scholar 

  29. Vaidya, J., Clifton, C.: Privacy preserving association rule mining in vertically partitioned data. In: Proc. of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 639–644 (2002)

    Google Scholar 

  30. Vaidya, J., Clifton, C.: Privacy-preserving k-means clustering over vertically partitioned data. In: Proc. of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 206–215 (2003)

    Google Scholar 

  31. Vaidya, J., Clifton, C.: Privacy preserving naive Bayes classifier on vertically partitioned data. In: 2004 SIAM International Conference on Data Mining (2004)

    Google Scholar 

  32. Vaidya, J., Clifton, C.: Privacy-preserving decision trees over vertically partitioned data. In: The 19th Annual IFIP WG 11.3 Working Conference on Data and Applications Security (2005)

    Google Scholar 

  33. Yang, Z., Subramaniam, H., Wright, R.N.: Experimental analysis of a privacy-preserving scalar product protocol. International Journal of Computer Systems Science and Engineering 21(1), 47–52 (2006)

    Google Scholar 

  34. Yang, Z., Wright, R.: Privacy-preserving computation of Bayesian networks on vertically partitioned data. IEEE Transactions on Data Knowledge Engineering 18(9) (2006)

    Google Scholar 

  35. Yao, A.: How to generate and exchange secrets. In: Proc. of the 27th IEEE Symposium on Foundations of Computer Science, pp. 162–167 (1986)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science, Stevens Institute of Technology, Hoboken, NJ, 07030, USA

    Zhiqiang Yang

  2. Department of Computer Science, SUNY Buffalo, Buffalo, NY, 14260, USA

    Sheng Zhong

  3. Department of Computer Science and DIMACS, Rutgers University, Piscataway, NJ, 08854, USA

    Rebecca N. Wright

Authors
  1. Zhiqiang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sheng Zhong
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rebecca N. Wright
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Francesco Bonchi Elena Ferrari Bradley Malin Yücel Saygin

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Yang, Z., Zhong, S., Wright, R.N. (2008). Towards Privacy-Preserving Model Selection. In: Bonchi, F., Ferrari, E., Malin, B., Saygin, Y. (eds) Privacy, Security, and Trust in KDD. PInKDD 2007. Lecture Notes in Computer Science, vol 4890. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78478-4_8

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-78478-4_8

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-78477-7

  • Online ISBN: 978-3-540-78478-4

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation