Definition
The problem of preserving privacy while allowing data analysis can be attacked in many ways. One way is to avoid disclosing data beyond its source while still constructing data mining models equivalent to those that would have been learned on an integrated data set. This follows the approach of Secure Multiparty Computation (SMC). SMC refers to the general problem of computing a given function securely over private inputs while revealing nothing extra to any party except what can be inferred (in polynomial time) from its input and output. Since one can prove that data are not disclosed beyond its original source, the opportunity for misuse is not increased by the process of data mining.
The definition of privacy followed in this line of research is conceptually simple: no site should learn anything new from the processof data mining. Specifically, anything learned during the data mining process must be derivable given one’s own data and the final result. In other words,...
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsRecommended Reading
Agrawal R. and Srikant R. Privacy-preserving data mining. In Proc. ACM SIGMOD Int. Conf. on Management of Data, 2000, pp. 439–450.
Du W. and Zhan Z. Building decision tree classifier on private data. In C. Clifton and V. Estivill-Castro (eds.). IEEE Int. Conf. on Data Mining Workshop on Privacy, Security, and Data Mining, 2002, pp. 1–8.
Freedman M.J., Nissim K., and Pinkas B. Efficient private matching and set intersection. In Proc. Int. Conf. Theory and Application of Cryptographic Techniques, 2004.
Goethals B., Laur S., Lipmaa H., and Mielikäinen T. On secure scalar product computation for privacy-preserving data mining. In Proc. the Seventh Annual Int. Conf. in Information Security and Cryptology, 2004, pp. 104–120.
Goldreich O. The Foundations of Cryptography, vol. 2, General Cryptographic Protocols. Cambridge University Press, London, 2004.
Goldreich O., Micali S., and Wigderson A. How to play any mental game – a completeness theorem for protocols with honest majority. In Proc. 19th ACM Symp. on the Theory of Computing, 1987, pp. 218–229.
Kantarcıoǧlu M. and Clifton C. Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans. Knowl. Data Eng., 16(9):1026–1037, 2004.
Lin X., Clifton C., and Zhu M. Privacy preserving clustering with distributed EM mixture modeling. Knowl. Inf. Syst., 8(1):68–81, 2005.
Lindell Y. and Pinkas B. Privacy preserving data mining. J. Cryptol., 15(3):177–206, 2002.
Naor M. and Pinkas B. Oblivious transfer and polynomial evaluation. In Proc. Thirty-First Annual ACM Symp. on Theory of Computing, 1999, pp. 245–254.
Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In Proc. Int. Conf. Theory and Application of Cryptographic Techniques, 1999, pp. 223–238.
Vaidya J. and Clifton C. Privacy-preserving outlier detection. In Proc. 2004 IEEE Int. Conf. on Data Mining, 2004, pp. 233–240.
Vaidya J. and Clifton C. Secure set intersection cardinality with application to association rule mining. J. Comput. Security, 13(4):593–622, November 2005.
Vaidya J., Clifton C., and Zhu M. Privacy-Preserving Data Mining, vol. 19 of Advances in Information Security, 1st edn. Springer, Berlin, 2005.
Yao A.C. How to generate and exchange secrets. In Proc. 27th IEEE Symp. on Foundations of Computer Science, 1986, pp. 162–167.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer Science+Business Media, LLC
About this entry
Cite this entry
Kantarcıoǧlu, M., Vaidya, J. (2009). Secure Multiparty Computation Methods. In: LIU, L., ÖZSU, M.T. (eds) Encyclopedia of Database Systems. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-39940-9_1388
Download citation
DOI: https://doi.org/10.1007/978-0-387-39940-9_1388
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-35544-3
Online ISBN: 978-0-387-39940-9
eBook Packages: Computer ScienceReference Module Computer Science and Engineering