Abstract
The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data is insufficient. More effective anonymization techniques are required. In this work, we propose a k-anonymization-based technique on set-valued network node data. The proposed algorithm is based on the principle of minimizing the number of addition and deletion operations to achieve k-anonymity. Numerical experiments on real dataset show that it requires less number of operations than current suppression-based approach.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010 |
| Pages | 809-812 |
| Number of pages | 4 |
| DOIs | |
| Publication status | Published - 2010 Dec 1 |
| Event | 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010 - Hangzhou, China Duration: 2010 Dec 18 → 2010 Dec 20 |
Publication series
| Name | Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010 |
|---|
Other
| Other | 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010 |
|---|---|
| Country | China |
| City | Hangzhou |
| Period | 10-12-18 → 10-12-20 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Computational Theory and Mathematics
- Theoretical Computer Science
Cite this
}
Anonymizing set-valued social data. / Wang, Shyue Liang; Tsai, Yu Chuan; Kao, Hung-Yu; Hong, Tzung Pei.
Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010. 2010. p. 809-812 5724922 (Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - Anonymizing set-valued social data
AU - Wang, Shyue Liang
AU - Tsai, Yu Chuan
AU - Kao, Hung-Yu
AU - Hong, Tzung Pei
PY - 2010/12/1
Y1 - 2010/12/1
N2 - The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data is insufficient. More effective anonymization techniques are required. In this work, we propose a k-anonymization-based technique on set-valued network node data. The proposed algorithm is based on the principle of minimizing the number of addition and deletion operations to achieve k-anonymity. Numerical experiments on real dataset show that it requires less number of operations than current suppression-based approach.
AB - The increasing popularity of social networks has generated tremendous amount of data to be exploited for commercial, research and many other valuable applications. However, the release of these data has raised an issue that personal privacy may be breached. Current practices of simply removing all identifiable personal information (such as names and social security numbers) before releasing the data is insufficient. More effective anonymization techniques are required. In this work, we propose a k-anonymization-based technique on set-valued network node data. The proposed algorithm is based on the principle of minimizing the number of addition and deletion operations to achieve k-anonymity. Numerical experiments on real dataset show that it requires less number of operations than current suppression-based approach.
UR - http://www.scopus.com/inward/record.url?scp=79953087401&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=79953087401&partnerID=8YFLogxK
U2 - 10.1109/GreenCom-CPSCom.2010.33
DO - 10.1109/GreenCom-CPSCom.2010.33
M3 - Conference contribution
SN - 9780769543314
T3 - Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010
SP - 809
EP - 812
BT - Proceedings - 2010 IEEE/ACM International Conference on Green Computing and Communications, GreenCom 2010, 2010 IEEE/ACM International Conference on Cyber, Physical and Social Computing, CPSCom 2010
ER -