Privacy preserving data mining algorithms by data distortion

Xiao Dan Wu; Dian Min Yue; Feng Li Liu; Yun Feng Wang; Chao Hsien Chu

doi:10.1109/ICMSE.2006.313871

Privacy preserving data mining algorithms by data distortion

Xiao Dan Wu, Dian Min Yue, Feng Li Liu, Yun Feng Wang, Chao Hsien Chu

College of Information Sciences and Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

9 Scopus citations

Abstract

Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.

Original language	English (US)
Title of host publication	Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	223-228
Number of pages	6
ISBN (Print)	7560323553, 9787560323558
DOIs	https://doi.org/10.1109/ICMSE.2006.313871
State	Published - Jan 1 2006
Event	2006 International Conference on Management Science and Engineering, ICMSE'06 - Lille, France Duration: Oct 5 2006 → Oct 7 2006

Publication series

Name	Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)

Other

Other	2006 International Conference on Management Science and Engineering, ICMSE'06
Country/Territory	France
City	Lille
Period	10/5/06 → 10/7/06

All Science Journal Classification (ASJC) codes

Management of Technology and Innovation
General Decision Sciences
Engineering (miscellaneous)

Access to Document

10.1109/ICMSE.2006.313871

Cite this

Wu, X. D., Yue, D. M., Liu, F. L., Wang, Y. F., & Chu, C. H. (2006). Privacy preserving data mining algorithms by data distortion. In Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th) (pp. 223-228). Article 4104898 (Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICMSE.2006.313871

Wu, Xiao Dan ; Yue, Dian Min ; Liu, Feng Li et al. / Privacy preserving data mining algorithms by data distortion. Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th). Institute of Electrical and Electronics Engineers Inc., 2006. pp. 223-228 (Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)).

@inproceedings{3c4ccf8b64bd45278a9e800ceb5edfb3,

title = "Privacy preserving data mining algorithms by data distortion",

abstract = "Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.",

author = "Wu, {Xiao Dan} and Yue, {Dian Min} and Liu, {Feng Li} and Wang, {Yun Feng} and Chu, {Chao Hsien}",

year = "2006",

month = jan,

day = "1",

doi = "10.1109/ICMSE.2006.313871",

language = "English (US)",

isbn = "7560323553",

series = "Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "223--228",

booktitle = "Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)",

address = "United States",

note = "2006 International Conference on Management Science and Engineering, ICMSE'06 ; Conference date: 05-10-2006 Through 07-10-2006",

}

Wu, XD, Yue, DM, Liu, FL, Wang, YF & Chu, CH 2006, Privacy preserving data mining algorithms by data distortion. in Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)., 4104898, Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th), Institute of Electrical and Electronics Engineers Inc., pp. 223-228, 2006 International Conference on Management Science and Engineering, ICMSE'06, Lille, France, 10/5/06. https://doi.org/10.1109/ICMSE.2006.313871

Privacy preserving data mining algorithms by data distortion. / Wu, Xiao Dan; Yue, Dian Min; Liu, Feng Li et al.
Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th). Institute of Electrical and Electronics Engineers Inc., 2006. p. 223-228 4104898 (Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - Privacy preserving data mining algorithms by data distortion

AU - Wu, Xiao Dan

AU - Yue, Dian Min

AU - Liu, Feng Li

AU - Wang, Yun Feng

AU - Chu, Chao Hsien

PY - 2006/1/1

Y1 - 2006/1/1

N2 - Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.

AB - Recently, a new class of data mining methods, known as privacy preserving data mining (PPDM) algorithms, has been developed by the research community working on security and knowledge discovery. The aim of these algorithms is the extraction of relevant knowledge from large amount of data, while protecting sensitive information simultaneously. In this paper, we present a generic PPDM framework and a classification scheme for centralized database, adopted from early studies, to guide the review process. Frequencies of different techniques/algorithms used are tableau and analyzed. A set of metrics and a theoretical framework are also proposed for assessing the relative performance of selected PPDM algorithms. Finally, we share directions for future research.

UR - http://www.scopus.com/inward/record.url?scp=43549116165&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=43549116165&partnerID=8YFLogxK

U2 - 10.1109/ICMSE.2006.313871

DO - 10.1109/ICMSE.2006.313871

M3 - Conference contribution

SN - 7560323553

SN - 9787560323558

T3 - Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)

SP - 223

EP - 228

BT - Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2006 International Conference on Management Science and Engineering, ICMSE'06

Y2 - 5 October 2006 through 7 October 2006

ER -

Wu XD, Yue DM, Liu FL, Wang YF, Chu CH. Privacy preserving data mining algorithms by data distortion. In Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th). Institute of Electrical and Electronics Engineers Inc. 2006. p. 223-228. 4104898. (Proceedings of 2006 International Conference on Management Science and Engineering, ICMSE'06 (13th)). doi: 10.1109/ICMSE.2006.313871

Privacy preserving data mining algorithms by data distortion

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this