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 | |
| 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 |
Fingerprint
Keywords
- Big data privacy
- Dynamic data re-publication
- M-invariance
- Privacy-preserving data publishing
ASJC Scopus subject areas
- Theoretical Computer Science
- Computer Science(all)
Cite this
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
}
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
ER -