Privacy preserving data mining research: Current status and key issues

Xiaodan Wu; Chao Hsien Chu; Yunfeng Wang; Fengli Liu; Dianmin Yue

doi:10.1007/978-3-540-72588-6_125

Privacy preserving data mining research: Current status and key issues

Xiaodan Wu, Chao Hsien Chu, Yunfeng Wang, Fengli Liu, Dianmin Yue

College of Information Sciences and Technology

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

18 Scopus citations

Abstract

Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.

Original language	English (US)
Title of host publication	Computational Science - ICCS 2007 - 7th International Conference, Proceedings
Publisher	Springer Verlag
Pages	762-772
Number of pages	11
Edition	PART 3
ISBN (Print)	9783540725879
DOIs	https://doi.org/10.1007/978-3-540-72588-6_125
State	Published - 2007
Event	7th International Conference on Computational Science, ICCS 2007 - Beijing, China Duration: May 27 2007 → May 30 2007

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 3
Volume	4489 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	7th International Conference on Computational Science, ICCS 2007
Country/Territory	China
City	Beijing
Period	5/27/07 → 5/30/07

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-540-72588-6_125

Cite this

Wu, X., Chu, C. H., Wang, Y., Liu, F., & Yue, D. (2007). Privacy preserving data mining research: Current status and key issues. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings (PART 3 ed., pp. 762-772). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3). Springer Verlag. https://doi.org/10.1007/978-3-540-72588-6_125

Wu, Xiaodan ; Chu, Chao Hsien ; Wang, Yunfeng et al. / Privacy preserving data mining research : Current status and key issues. Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. Springer Verlag, 2007. pp. 762-772 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3).

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title = "Privacy preserving data mining research: Current status and key issues",

abstract = "Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.",

author = "Xiaodan Wu and Chu, {Chao Hsien} and Yunfeng Wang and Fengli Liu and Dianmin Yue",

year = "2007",

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booktitle = "Computational Science - ICCS 2007 - 7th International Conference, Proceedings",

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Wu, X, Chu, CH, Wang, Y, Liu, F & Yue, D 2007, Privacy preserving data mining research: Current status and key issues. in Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 3, vol. 4489 LNCS, Springer Verlag, pp. 762-772, 7th International Conference on Computational Science, ICCS 2007, Beijing, China, 5/27/07. https://doi.org/10.1007/978-3-540-72588-6_125

Privacy preserving data mining research: Current status and key issues. / Wu, Xiaodan; Chu, Chao Hsien; Wang, Yunfeng et al.
Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3. ed. Springer Verlag, 2007. p. 762-772 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4489 LNCS, No. PART 3).

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

TY - GEN

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T2 - 7th International Conference on Computational Science, ICCS 2007

AU - Wu, Xiaodan

AU - Chu, Chao Hsien

AU - Wang, Yunfeng

AU - Liu, Fengli

AU - Yue, Dianmin

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N2 - Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.

AB - Recent advances in the Internet, in data mining, and in security technologies have gave rise to a new stream of research, known as privacy preserving data mining (PPDM). PPDM technologies allow us to extract relevant knowledge from a large amount of data, while hide sensitive data or information from disclosure. Several research questions have often being asked: (1) what kind of option available for privacy preserving? (2) Which method is more popular? (3) how to measure the performance of these algorithms? And (4) how effective of these algorithms in preserving privacy? To help answer these questions, we conduct an extensive review of 29 recent references from years 2000 to 2006 for analysis.

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Wu X, Chu CH, Wang Y, Liu F, Yue D. Privacy preserving data mining research: Current status and key issues. In Computational Science - ICCS 2007 - 7th International Conference, Proceedings. PART 3 ed. Springer Verlag. 2007. p. 762-772. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 3). doi: 10.1007/978-3-540-72588-6_125

Privacy preserving data mining research: Current status and key issues

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