Incremental privacy-preserving association rule mining using negative border

Duc H. Tran; Wee Keong Ng; Y. D. Wong; Vinh V. Thai

doi:10.1007/978-3-319-31863-9_7

Incremental privacy-preserving association rule mining using negative border

Duc H. Tran^*, Wee Keong Ng, Y. D. Wong, Vinh V. Thai

^*Corresponding author for this work

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

Abstract

Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.

Original language	English
Title of host publication	Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings
Editors	Michael Chau, G. Alan Wang, Hsinchun Chen
Publisher	Springer Verlag
Pages	87-100
Number of pages	14
ISBN (Print)	9783319318622
DOIs	https://doi.org/10.1007/978-3-319-31863-9_7
Publication status	Published - 2016
Externally published	Yes
Event	11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016 - Auckland, New Zealand Duration: Apr 19 2016 → Apr 19 2016

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	9650
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016
Country/Territory	New Zealand
City	Auckland
Period	4/19/16 → 4/19/16

Bibliographical note

Publisher Copyright:
© Springer International Publishing Switzerland 2016.

ASJC Scopus Subject Areas

Theoretical Computer Science
General Computer Science

Keywords

Association rule mining
Incremental
Negative border
Privacy preserving
Secure protocol

Access to Document

10.1007/978-3-319-31863-9_7

Cite this

Tran, D. H., Ng, W. K., Wong, Y. D., & Thai, V. V. (2016). Incremental privacy-preserving association rule mining using negative border. In M. Chau, G. A. Wang, & H. Chen (Eds.), Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings (pp. 87-100). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9650). Springer Verlag. https://doi.org/10.1007/978-3-319-31863-9_7

Tran, Duc H. ; Ng, Wee Keong ; Wong, Y. D. et al. / Incremental privacy-preserving association rule mining using negative border. Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings. editor / Michael Chau ; G. Alan Wang ; Hsinchun Chen. Springer Verlag, 2016. pp. 87-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.",

keywords = "Association rule mining, Incremental, Negative border, Privacy preserving, Secure protocol",

author = "Tran, {Duc H.} and Ng, {Wee Keong} and Wong, {Y. D.} and Thai, {Vinh V.}",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2016.; 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016 ; Conference date: 19-04-2016 Through 19-04-2016",

year = "2016",

doi = "10.1007/978-3-319-31863-9_7",

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isbn = "9783319318622",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Tran, DH, Ng, WK, Wong, YD & Thai, VV 2016, Incremental privacy-preserving association rule mining using negative border. in M Chau, GA Wang & H Chen (eds), Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 9650, Springer Verlag, pp. 87-100, 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016, Auckland, New Zealand, 4/19/16. https://doi.org/10.1007/978-3-319-31863-9_7

Incremental privacy-preserving association rule mining using negative border. / Tran, Duc H.; Ng, Wee Keong; Wong, Y. D. et al.
Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings. ed. / Michael Chau; G. Alan Wang; Hsinchun Chen. Springer Verlag, 2016. p. 87-100 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 9650).

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

TY - GEN

T1 - Incremental privacy-preserving association rule mining using negative border

AU - Tran, Duc H.

AU - Ng, Wee Keong

AU - Wong, Y. D.

AU - Thai, Vinh V.

N1 - Publisher Copyright: © Springer International Publishing Switzerland 2016.

PY - 2016

Y1 - 2016

N2 - Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.

AB - Privacy preserving association rule mining can extract important rules from distributed data with limited privacy breaches. Protecting privacy in incremental maintenance for distributed association rule mining is necessary since data are frequently updated. In privacy preserving data mining, scanning all the distributed data is very costly. This paper proposes a new incremental protocol for privacy preserving association rule mining using negative border concept. The protocol scans old databases at most once, and therefore reducing the I/O time. We also conduct experiments to show the efficiency of our protocol over existing ones.

KW - Association rule mining

KW - Incremental

KW - Negative border

KW - Privacy preserving

KW - Secure protocol

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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BT - Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings

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A2 - Wang, G. Alan

A2 - Chen, Hsinchun

PB - Springer Verlag

T2 - 11th Pacific Asia Workshop on Intelligence and Security Informatics, PAISI 2016

Y2 - 19 April 2016 through 19 April 2016

ER -

Tran DH, Ng WK, Wong YD, Thai VV. Incremental privacy-preserving association rule mining using negative border. In Chau M, Wang GA, Chen H, editors, Intelligence and Security Informatics - 11th Pacific Asia Workshop, PAISI 2016, Proceedings. Springer Verlag. 2016. p. 87-100. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-319-31863-9_7

Incremental privacy-preserving association rule mining using negative border

Abstract

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Bibliographical note

ASJC Scopus Subject Areas

Keywords

Access to Document

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