TITLE:
A Dynamic Social Network Data Publishing Algorithm Based on Differential Privacy
AUTHORS:
Zhenpeng Liu, Yawei Dong, Xuan Zhao, Bin Zhang
KEYWORDS:
Dynamic Social Network, Data Publishing, Differential Privacy
JOURNAL NAME:
Journal of Information Security,
Vol.8 No.4,
October
20,
2017
ABSTRACT: Social network contains the interaction between social members, which constitutes
the structure and attribute of social network. The interactive relationship
of social network contains a lot of personal privacy information. The direct
release of social network data will cause the disclosure of privacy information.
Aiming at the dynamic characteristics of social network data release, a
new dynamic social network data publishing method based on differential
privacy was proposed. This method was consistent with differential privacy. It
is named DDPA (Dynamic Differential Privacy Algorithm). DDPA algorithm
is an improvement of privacy protection algorithm in static social network
data publishing. DDPA adds noise which follows Laplace to network edge
weights. DDPA identifies the edge weight information that changes as the
number of iterations increases, adding the privacy protection budget. Through
experiments on real data sets, the results show that the DDPA algorithm satisfies
the user’s privacy requirement in social network. DDPA reduces the execution
time brought by iterations and reduces the information loss rate of
graph structure.