Protecting Sensitive
Labels in Social Network Data Anonymization
ABSTRACT:
Privacy is one of the major concerns when publishing
or sharing social network data for social science research and business
analysis. Recently, researchers have developed privacy models similar to
k-anonymity to prevent node reidentification through structure information.
However, even when these privacy models are enforced, an attacker may still be
able to infer one’s private information if a group of nodes largely share the
same sensitive labels (i.e., attributes). In other words, the label-node relationship
is not well protected by pure structure anonymization methods. Furthermore,
existing approaches, which rely on edge editing or node clustering, may
significantly alter key graph properties. In this paper, we define a k-degree-l-diversity anonymity model that considers the protection of
structural information as well as sensitive labels of individuals. We further
propose a novel anonymization methodology based on adding noise nodes. We
develop a new algorithm by adding noise nodes into the original graph with the
consideration of introducing the least distortion to graph properties. Most
importantly, we provide a rigorous analysis of the theoretical bounds on the
number of noise nodes added and their impacts on an important graph property.
We conduct extensive experiments to evaluate the effectiveness of the proposed
technique.
EXISTING SYSTEM:
Recently, much work has been done on
anonymizing tabular microdata. A variety of privacy models as well as anonymization
algorithms have been developed (e.g., kanonymity, l-diversity, t-closeness. In
tabular microdata, some of the nonsensitive attributes, called quasi identifiers,
can be used to reidentify individuals and their sensitive attributes. When
publishing social network data,graph structures are also published with
corresponding social relationships. As a result, it may be exploited as a new
means to compromise privacy.
DISADVANTAGES
OF EXISTING SYSTEM:
·
The edge-editing method sometimes may
change the distance properties substantially by connecting two faraway nodes
together or deleting the bridge link between two communities.
·
Mining over these data might get the
wrong conclusion about how the salaries are distributed in the society.
Therefore, solely relying on edge editing may not be a good solution to
preserve data utility.
PROPOSED SYSTEM:
We propose a novel idea to preserve
important graph properties, such as distances between nodes by adding certain
“noise” nodes into a graph. This idea is based on the following key
observation.
In Our proposed system, privacy
preserving goal is to prevent an attacker from reidentifying a user and finding
the fact that a certain user has a specific sensitive value. To achieve this
goal, we define a k-degree-l-diversity (KDLD) model for safely
publishing a labeled graph, and then develop corresponding graph anonymization
algorithms with the least distortion to the properties of the original graph,
such as degrees and distances between nodes.
ADVANTAGES
OF PROPOSED SYSTEM:
v We
combine k-degree anonymity with l-diversity to prevent not only the
reidentification of individual nodes but also the revelation of a sensitive
attribute associated with each node.
v We
propose a novel graph construction technique which makes use of noise nodes to
preserve utilities of the original graph. Two key properties are considered: 1)
Add as few noise edges as possible; 2) Change the distance between nodes as
less as possible.
v We
present analytical results to show the relationship between the number of noise
nodes added and their impacts on an important graph property.
SYSTEM ARCHITECTURE:
SYSTEM CONFIGURATION:-
HARDWARE CONFIGURATION:-
ü Processor - Pentium –IV
ü Speed - 1.1
Ghz
ü RAM - 256
MB(min)
ü Hard
Disk - 20
GB
ü Key
Board - Standard
Windows Keyboard
ü Mouse - Two
or Three Button Mouse
ü Monitor - SVGA
SOFTWARE CONFIGURATION:-
ü Operating System :
Windows XP
ü Programming Language : JAVA
ü Java Version : JDK 1.6 & above.
REFERENCE:
Mingxuan Yuan, Lei Chen, Member, IEEE, Philip S. Yu,
Fellow, IEEE, and Ting Yu-“Protecting Sensitive Labels in Social Network Data
Anonymization”-IEEE TRANSACTIONS ON
KNOWLEDGE AND DATA ENGINEERING, VOL. 25, NO. 3, MARCH 2013.