Prevent re-identification attacks by adversaries with immediate neighborhood structural knowledge

The publication of social network data entails a privacy threat for their users. Sensitive information about users of the social networks should be protected. The challenge is to devise methods to publish social network data in a form that affords utility without compromising privacy. Previous research has proposed various privacy models with the corresponding protection mechanisms that prevent both inadvertent private information leakage and attacks by malicious adversaries. These early privacy models are mostly concerned with identity and link disclosure. The social networks are modeled as graphs in which users are nodes and social connections are edges. The threat definitions and protection mechanisms leverage structural properties of the graph. The recognition of the need for a finer grain and more personalized privacy. A privacy protection scheme that not only prevents the disclosure of identity of users but also the disclosure of selected features in users’ profiles. An individual user can select which features of her profile she wishes to conceal. The social networks are modeled as graphs in which users are nodes and features are labels. Labels are denoted either as sensitive or as non-sensitive. A labeled graph representing a small subset of such a social network. the graph represents a user, and the edge between two nodes represents the fact that the two persons are friends. Labels annotated to the nodes show the locations of users. Each letter represents a city name as a label for each node. Some individuals do not mind their residence being known by the others, but some do, for various reasons. The contexts of micro- and network data consists in removing identification. This nave technique has quickly been recognized as failing to protect privacy. Micro data, Sweeney et al. propose k-anonymity to circumvent possible identity disclosure in naively anonymized micro data. `Diversity is proposed in order to further prevent attribute disclosure.