Corruption
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.
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.