TY - CONF
T1 - A new scheme on privacy-preserving classification
AU - Zhang, Nan
AU - Wang, Shengquan
AU - Zhao, Wei
N1 - Funding Information:
In this paper, we address issues related to privacy-preserving data mining. In particular, we focus on privacy-preserving data classification. General classification techniques have been extensively ∗This work was supported in part by the National Science Foundation under Contracts 0081761, 0324988, 0329181, by the Defense Advanced Research Projects Agency under Contract F30602-99-1-0531, and by Texas A&M University under its Telecommunication and Information Task Force Program. Any opinions, findings, conclusions, and/or recommendations expressed in this material, either expressed or implied, are those of the authors and do not necessarily reflect the views of the sponsors listed above.
PY - 2005
Y1 - 2005
N2 - We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this approach is now proven to be insecure as it has been discovered that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. We introduce an algebraic-technique-based scheme. Compared to the randomization approach, our new scheme can build classifiers more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.
AB - We address privacy-preserving classification problem in a distributed system. Randomization has been the approach proposed to preserve privacy in such scenario. However, this approach is now proven to be insecure as it has been discovered that some privacy intrusion techniques can be used to reconstruct private information from the randomized data tuples. We introduce an algebraic-technique-based scheme. Compared to the randomization approach, our new scheme can build classifiers more accurately but disclose less private information. Furthermore, our new scheme can be readily integrated as a middleware with existing systems.
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M3 - Paper
AN - SCOPUS:32344442645
SP - 374
EP - 383
T2 - KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 21 August 2005 through 24 August 2005
ER -