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,...
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Agrawal R, Srikant R. Privacy-preserving data mining. In: Proceedings of the ACM SIGMOD International Conference on Management of Data; 2000. p. 439–50.
Du W, Zhan Z. Building decision tree classifier on private data. In: Clifton C, Estivill-Castro V, editors. Proceedings of the IEEE International Conference on Data Mining Workshop on Privacy, Security, and Data Mining; 2002. p. 1–8.
Freedman MJ, Nissim K, Pinkas B. Efficient private matching and set intersection. In: Proceedings of the International Conference on Theory and Application of Cryptographic Techniques; 2004.
Goethals B, Laur S, Lipmaa H, Mielikäinen T. On secure scalar product computation for privacy-preserving data mining. In: Proceedings of the 7th Annual International Conference in Information Security and Cryptology; 2004. p. 104–20.
Goldreich O. The foundations of cryptography, General cryptographic protocols, vol. 2. London: Cambridge University Press; 2004.
Goldreich O, Micali S, Wigderson A. How to play any mental game – a completeness theorem for protocols with honest majority. In: Proceedings of the 19th ACM Symposium on the Theory of Computing; 1987. p. 218–29.
Kantarcolu M, Clifton C. Privacy-preserving distributed mining of association rules on horizontally partitioned data. IEEE Trans Knowl Data Eng. 2004;16(9):1026–37.
Lin X, Clifton C, Zhu M. Privacy preserving clustering with distributed EM mixture modeling. Knowl Inf Syst. 2005;8(1):68–81.
Lindell Y, Pinkas B. Privacy preserving data mining. J Cryptol. 2002;15(3):177–206.
Naor M, Pinkas B. Oblivious transfer and polynomial evaluation. In: Proceedings of the 31st Annual ACM Symposium on Theory of Computing; 1999. p. 245–54.
Paillier P. Public-key cryptosystems based on composite degree residuosity classes. In: Proceedings of the International Conference on Theory and Application of Cryptographic Techniques; 1999. p. 223–38.
Vaidya J, Clifton C. Privacy-preserving outlier detection. In: Proceedings of the 4th IEEE International Conference on Data Mining; 2004. p. 233–240.
Vaidya J, Clifton C. Secure set intersection cardinality with application to association rule mining. J Comput Security. 2005;13(4):593–622.
Vaidya J, Clifton C, Zhu M. Privacy-preserving data mining. In: Advances in information security, vol. 19. 1st ed. Berlin: Springer; 2005.
Yao AC. How to generate and exchange secrets. In: Proceedings of the 27th IEEE Symposium on Foundations of Computer Science; 1986. p. 162–7.
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Kantarcolu, M., Vaidya, J. (2018). Secure Multiparty Computation Methods. In: Liu, L., Özsu, M.T. (eds) Encyclopedia of Database Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8265-9_1388
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DOI: https://doi.org/10.1007/978-1-4614-8265-9_1388
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