A new scheme on privacy-preserving classification

Nan Zhang; Shengquan Wang; Wei Zhao

A new scheme on privacy-preserving classification

Nan Zhang
, Shengquan Wang
, Wei Zhao

College of Information Sciences and Technology

Research output: Contribution to conference › Paper › peer-review

53 Scopus citations

Abstract

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.

Original language	English (US)
Pages	374-383
Number of pages	10
State	Published - 2005
Event	KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - Chicago, IL, United States Duration: Aug 21 2005 → Aug 24 2005

Other

Other	KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Country/Territory	United States
City	Chicago, IL
Period	8/21/05 → 8/24/05

All Science Journal Classification (ASJC) codes

Information Systems
Software

Cite this

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title = "A new scheme on privacy-preserving classification",

abstract = "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.",

author = "Nan Zhang and Shengquan Wang and Wei Zhao",

note = "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.; KDD-2005: 11th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining ; Conference date: 21-08-2005 Through 24-08-2005",

year = "2005",

language = "English (US)",

pages = "374--383",

}

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 -

A new scheme on privacy-preserving classification

Abstract

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All Science Journal Classification (ASJC) codes

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