Privacy-Preserving Data Mining Rakesh Agrawal and Ramakrishnan Srikant Return to Research Sessions 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 been perturbed. The resulting data records look very different from the original records and the distribution of data values is also very different from the original distribution. While it is not possible to accurately estimate original values in individual data records, we propose a novel reconstruction procedure to accurately estimate the distribution of original data values. 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SIGMOD Conference 1977 : 169-181 BIBTEX @inproceedings{DBLP:conf/sigmod/AgrawalS00,   author    = {Rakesh Agrawal and                 Ramakrishnan Srikant},    editor    = {Weidong Chen and                 Jeffrey F. Naughton and                 Philip A. Bernstein},    title     = {Privacy-Preserving Data Mining},    booktitle = {Proceedings of the 2000 ACM SIGMOD International Conference on                 Management of Data, May 16-18, 2000, Dallas, Texas, USA},    journal   = {SIGMOD Record},    publisher = {ACM},    volume    = {29},    number    = {2},    year      = {2000},    isbn      = {1-58113-218-2},    pages     = {439-450},    crossref  = {DBLP:conf/sigmod/2000},    bibsource = {DBLP, http://dblp.uni-trier.de} } }, DiSC'01 Copyright ©2002 ACM Inc.