In this article we provide a formal framework for reidentification in general. We define n-confusion as a concept for modelling the anonymity of a database table and we prove that n-confusion is a generalization of kanonymity. After a short survey on the different available definitions of kanonymity for graphs we provide a new definition for k-anonymous graph, which we consider to be the correc...

In recent years, privacy concerns about social network graph data publishing has increased due to the widespread use of such data for research purposes. This paper addresses the problem of identity disclosure risk of a node assuming that the adversary identifies one of its immediate neighbors in the published data. The related anonymity level of a graph is formulated and a mathematical model is...

We study the challenges of protecting privacy of individuals in the large public survey rating data in this chapter. Recent study shows that personal information in supposedly anonymous movie rating records is de-identified. The survey rating data usually contains both ratings of sensitive and non-sensitive issues. The ratings of sensitive issues involve personal privacy. Even though the survey...

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iv LIST OF TABLES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . viii LIST OF FIGURES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ix CHAPTER

The problem of privacy-preserving data mining has attracted considerable attention in recent years because of increasing concerns about the privacy of the underlying data. In recent years, an important data domain which has emerged is that of graphs and structured data. Many data sets such as XML data, transportation networks, traffic in IP networks, social networks and hierarchically structure...

Data anonymization is of increasing importance for allowing sharing of individual data for a variety of data analysis and mining applications. Most of existing work on data anonymization optimizes the anonymization in terms of data utility typically through one-size-fits-all measures such as data discernibility. Our primary viewpoint in this paper is that each target application may have a uniq...

In this paper, we conduct the first comprehensive quantification on the perfect de-anonymizability and partial deanonymizability of real world social networks with seed information in general scenarios, where a social network can follow an arbitrary distribution model. This quantification provides the theoretical foundation for existing structure based de-anonymization attacks (e.g., [1][2][3])...

Social networking is gaining enormous popularity in the past few years. However, the popularity may also bring unexpected consequences for users regarding safety and privacy concerns. To prevent privacy being breached and modeling a social network as a weighted graph, many effective anonymization techniques have been proposed. In this work, we consider the edge weight anonymity problem. In part...