TY - GEN
T1 - Probabilistic privacy analysis of published views
AU - Wang, Hui
AU - Lakshmanan, Laks V.S.
PY - 2006
Y1 - 2006
N2 - Among techniques for ensuring privacy in data publishing, k-anonymity and publishing of views on private data are quite popular. In this paper, we consider data publishing by views and develop a probability framework for the analysis of privacy breach. We propose two attack models and derive the probability of privacy breach for each model.
AB - Among techniques for ensuring privacy in data publishing, k-anonymity and publishing of views on private data are quite popular. In this paper, we consider data publishing by views and develop a probability framework for the analysis of privacy breach. We propose two attack models and derive the probability of privacy breach for each model.
KW - privacy breach
KW - private association
KW - probabilistic analysis
KW - published views
UR - http://www.scopus.com/inward/record.url?scp=84885218187&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885218187&partnerID=8YFLogxK
U2 - 10.1145/1179601.1179616
DO - 10.1145/1179601.1179616
M3 - Conference contribution
AN - SCOPUS:84885218187
SN - 1595935568
SN - 9781595935564
T3 - Proceedings of the ACM Conference on Computer and Communications Security
SP - 81
EP - 84
BT - Proceedings of the 5th ACM Workshop on Privacy in Electronic Society, WPES 2006, Co-located with the 13th ACM Conference on Computer and Communications Security, CCS 2006
T2 - 5th ACM Workshop on Privacy in Electronic Society, WPES 2006, Co-located with the 13th ACM Conference on Computer and Communications Security, CCS 2006
Y2 - 30 October 2006 through 30 October 2006
ER -