Secure re-publication of dynamic big data

Kok Seng Wong; Myung Ho Kim

doi:10.1007/978-3-319-03584-0_36

Secure re-publication of dynamic big data

Kok Seng Wong, Myung Ho Kim

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

1 Citation (Scopus)

Abstract

Dynamic data re-publication is now an emerging issue in data publishing due to the awareness of privacy disclosure in data sharing. Existing models such as k-anonymity and (-diversity only aim to provide data protection for single release. In practical, new data arrive continuously and up-to-date dataset should be released from time to time. The release of multiple anony-mized datasets (microdata) allows the attackers to learn extra knowledge by cross examines the releases within a targeted timeframe. In this paper, we study the data re-publication of dynamic big data based on the m-invariance model. In particular, we reconstruct the existing model to support re-publication for big data. We consider re-publication with insertion, deletion and update of the existing records. Counterfeit records will be used to maintain the update pattern of all the releases and to increase the false information in the knowledge learned by the attacker from the released microdata.

Original language	English
Title of host publication	Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings
Pages	468-477
Number of pages	10
DOIs	https://doi.org/10.1007/978-3-319-03584-0_36
Publication status	Published - Dec 1 2013
Externally published	Yes
Event	5th International Symposium on Cyberspace Safety and Security, CSS 2013 - Zhangjiajie, China Duration: Nov 13 2013 → Nov 15 2013

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	8300 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	5th International Symposium on Cyberspace Safety and Security, CSS 2013
Country	China
City	Zhangjiajie
Period	11/13/13 → 11/15/13

Keywords

Big data privacy
Dynamic data re-publication
M-invariance
Privacy-preserving data publishing

ASJC Scopus subject areas

Theoretical Computer Science
Computer Science(all)

Access to Document

10.1007/978-3-319-03584-0_36

Fingerprint Dive into the research topics of 'Secure re-publication of dynamic big data'. Together they form a unique fingerprint.

Cite this

Wong, K. S., & Kim, M. H. (2013). Secure re-publication of dynamic big data. In Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings (pp. 468-477). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8300 LNCS). https://doi.org/10.1007/978-3-319-03584-0_36

Secure re-publication of dynamic big data. / Wong, Kok Seng; Kim, Myung Ho.

Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings. 2013. p. 468-477 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8300 LNCS).

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

Wong, KS & Kim, MH 2013, Secure re-publication of dynamic big data. in Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8300 LNCS, pp. 468-477, 5th International Symposium on Cyberspace Safety and Security, CSS 2013, Zhangjiajie, China, 11/13/13. https://doi.org/10.1007/978-3-319-03584-0_36

@inproceedings{cc436f2f9199463fbffed2bc49eda04c,

title = "Secure re-publication of dynamic big data",

abstract = "Dynamic data re-publication is now an emerging issue in data publishing due to the awareness of privacy disclosure in data sharing. Existing models such as k-anonymity and (-diversity only aim to provide data protection for single release. In practical, new data arrive continuously and up-to-date dataset should be released from time to time. The release of multiple anony-mized datasets (microdata) allows the attackers to learn extra knowledge by cross examines the releases within a targeted timeframe. In this paper, we study the data re-publication of dynamic big data based on the m-invariance model. In particular, we reconstruct the existing model to support re-publication for big data. We consider re-publication with insertion, deletion and update of the existing records. Counterfeit records will be used to maintain the update pattern of all the releases and to increase the false information in the knowledge learned by the attacker from the released microdata.",

keywords = "Big data privacy, Dynamic data re-publication, M-invariance, Privacy-preserving data publishing",

author = "Wong, {Kok Seng} and Kim, {Myung Ho}",

year = "2013",

month = dec,

day = "1",

doi = "10.1007/978-3-319-03584-0_36",

language = "English",

isbn = "9783319035833",

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

pages = "468--477",

booktitle = "Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings",

}

TY - GEN

T1 - Secure re-publication of dynamic big data

AU - Wong, Kok Seng

AU - Kim, Myung Ho

PY - 2013/12/1

Y1 - 2013/12/1

N2 - Dynamic data re-publication is now an emerging issue in data publishing due to the awareness of privacy disclosure in data sharing. Existing models such as k-anonymity and (-diversity only aim to provide data protection for single release. In practical, new data arrive continuously and up-to-date dataset should be released from time to time. The release of multiple anony-mized datasets (microdata) allows the attackers to learn extra knowledge by cross examines the releases within a targeted timeframe. In this paper, we study the data re-publication of dynamic big data based on the m-invariance model. In particular, we reconstruct the existing model to support re-publication for big data. We consider re-publication with insertion, deletion and update of the existing records. Counterfeit records will be used to maintain the update pattern of all the releases and to increase the false information in the knowledge learned by the attacker from the released microdata.

AB - Dynamic data re-publication is now an emerging issue in data publishing due to the awareness of privacy disclosure in data sharing. Existing models such as k-anonymity and (-diversity only aim to provide data protection for single release. In practical, new data arrive continuously and up-to-date dataset should be released from time to time. The release of multiple anony-mized datasets (microdata) allows the attackers to learn extra knowledge by cross examines the releases within a targeted timeframe. In this paper, we study the data re-publication of dynamic big data based on the m-invariance model. In particular, we reconstruct the existing model to support re-publication for big data. We consider re-publication with insertion, deletion and update of the existing records. Counterfeit records will be used to maintain the update pattern of all the releases and to increase the false information in the knowledge learned by the attacker from the released microdata.

KW - Big data privacy

KW - Dynamic data re-publication

KW - M-invariance

KW - Privacy-preserving data publishing

UR - http://www.scopus.com/inward/record.url?scp=84894150684&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84894150684&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-03584-0_36

DO - 10.1007/978-3-319-03584-0_36

M3 - Conference contribution

AN - SCOPUS:84894150684

SN - 9783319035833

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

SP - 468

EP - 477

BT - Cyberspace Safety and Security - 5th International Symposium, CSS 2013, Proceedings

T2 - 5th International Symposium on Cyberspace Safety and Security, CSS 2013

Y2 - 13 November 2013 through 15 November 2013

ER -