Article history: Received 27 April 2010 Received in revised form 21 June 2011 Accepted 21 June 2011 Available online 2 July 2011 We present a multidisciplinary solution to the problems of anonymous microaggregation and clustering, illustrated with two applications, namely privacy protection in databases, and private retrieval of location-based information. Our solution is perturbative, is based...

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...

This article proposes a new technique for Privacy Preserving Collaborative Filtering (PPCF) based on microaggregation, which provides accurate recommendations estimated from perturbed data whilst guaranteeing user k-anonymity. The experimental results presented in this article show the effectiveness of the proposed technique in protecting users’ privacy without compromising the quality of the r...

Privacy issues during data publishing is an increasing concern of involved entities. The problem is addressed in the field of statistical disclosure control with the aim of producing protected datasets that are also useful for interested end users such as government agencies and research communities. The problem of producing useful protected datasets is addressed in multiple computational priva...

Abstract Unstructured texts are a very popular data type and still widely unexplored in the privacy preserving data mining field. We consider the problem of providing public information about a set of confidential documents. To that end we have developed a method to protect a Vector Space Model (VSM), to make it public even if the documents it represents are private. This method is inspired by ...