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Abstract: A fruitful direction for future data mining research will be the development of techniques that incorporate privacy concerns. Specifically, we address the following question. Since the primary task in data mining is the development of models about aggregated data, can we develop accurate models without access to precise information in individual data records? We consider the concrete case of building a decision-tree classifier from training data in which the values of individual records have... (Update)
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BibTeX entry: (Update)
R. Agrawal and S. Ramakrishnan. Privacy-preserving data mining. In Proceedings of of the 2000 ACM SIGMOD International Conference on Management of Data, pp. 439--450, 2000. http://citeseer.ist.psu.edu/agrawal00privacypreserving.html More
@inproceedings{ agrawal-preserving-data-mining,
author = "Rakesh Agrawal and Ramakrishnan Srikant",
title = "Privacy-preserving data mining",
booktitle = "Proc. of the ACM SIGMOD Conference on Management of Data",
pages = "439--450",
year = "2000",
month = May,
location = "Dallas, Texas",
isbn = { 1-581-13218-2},
publisher = {ACM Press},
url = "citeseer.ist.psu.edu/agrawal00privacypreserving.html",
url = "http://www.almaden.ibm.com/cs/people/srikant/papers/sigmod00.pdf" }
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