Closeness is described as a privacy measure and its advantages are illustrated through examples and experiments on a real dataset. In this Paper the closeness can be verified by giving different values for N and T. Government agencies and other organizations often need to publish micro data, e. g. , medical data or census data, for research and other purposes. Typically, such data are stored in...

Devising methods to publish social network data in a form that affords utility without compromising privacy remains a longstanding challenge, while many existing methods based on k-anonymity algorithms on social networks may result in nontrivial utility loss without analyzing the social network topological structure and without considering the attributes of sparse distribution. Toward this obje...

The anonymization of sensitive microdata (e.g. medical health records) is a widely-studied topic in the research community. A still unsolved problem is the limited informative value of anonymized microdata that often rules out further processing (e.g. statistical analysis). Thus, a tradeoff between anonymity and data precision has to be made, resulting in the release of partially anonymized mic...

Anonymity is critical for many networked applications. Yet current Internet protocols provide no support for masking the identity of communication endpoints. This paper outlines a design for a peer-to-peer, scalable, tamper-resilient communication protocol that provides strong anonymity and privacy. Called CliqueNet, our protocol provides an information-theoretic guarantee: an omnipotent advers...

In this note, we describe a method of simulated annealing for producing k-anonymity. For analytic purposes, there is no reason to expect that the method will be superior or worse than the method of applying genetic algorithms used by Iyengar (2002). The main appeal of simulated annealing is the amount of control it allows of the microaggregation process. The k-anonymity problem is known to be N...

In this paper, we cast the classic problem of achieving k-anonymity for a given database as a problem in algebraic topology. Using techniques from this field of mathematics, we propose a framework for k-anonymity that brings new insights and algorithms to anonymize a database. We begin by addressing the simpler case when the data lies in a metric space. This case is instrumental to introduce th...