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Cryptographic techniques for privacy-preserving data mining
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Volume 4 ,  Issue 2  (December 2002) table of contents
Pages: 12 - 19  
Year of Publication: 2002
ISSN:1931-0145
Author
Benny Pinkas  HP Labs
Publisher
ACM  New York, NY, USA
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ABSTRACT

Research in secure distributed computation, which was done as part of a larger body of research in the theory of cryptography, has achieved remarkable results. It was shown that non-trusting parties can jointly compute functions of their different inputs while ensuring that no party learns anything but the defined output of the function. These results were shown using generic constructions that can be applied to any function that has an efficient representation as a circuit. We describe these results, discuss their efficiency, and demonstrate their relevance to privacy preserving computation of data mining algorithms. We also show examples of secure computation of data mining algorithms that use these generic constructions.


REFERENCES

Note: OCR errors may be found in this Reference List extracted from the full text article. ACM has opted to expose the complete List rather than only correct and linked references.

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R. Cramer, Introduction to Secure Computation, 2000. Available at http://www.brics.dk/~cramer/papers/CRAMER_revised.ps.
 
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Wei Dai, The Crypto++ library, benchmark of Nov. 3, 2002, http://www.eskimo.com/weidai/cryptlib.html.
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O. Goldreich, Secure Multi-Party Computation, manuscript, 2002. Available at http://www.wisdom.weizmann.ac.il/oded/pp.html.
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Y. Lindell and B. Pinkas, Privacy Preserving Data Mining, Journal of Cryptology, Vol. 15, No. 3, pp. 177--206, 2002.
 
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M. O. Rabin, How to exchange secrets by oblivious transfer, Technical Memo TR-81, Aiken Computation Laboratory, 1981.
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A. C. Yao, How to generate and exchange secrets, Proceedings 27th Symposium on Foundations of Computer Science (FOCS), IEEE, 1986, pp. 162--167.

CITED BY  23