Data Protection: Too Personal to protect?

Orchestrating Transatlantic Approaches to Personal Data Protection: A European Perspective

The purpose of this Essay is to examine current EU and U.S. approaches to data protection in the context of the debate about transborder data flows. Part I begins by outlining the EU approach and the criteria governing data transfers to third countries. Part II examines the scope for selfregulation by organizations to safeguard personal data. Part III reviews the main features of the U.S. model...

When 25 Cents is Too Much: An Experiment on Willingness-To-Sell and Willingness-To-Protect Personal Information

We present an empirical study of willingness-to-pay for protecting information (we term it willingness-to-protect) and willingness-to-accept a proposal to sell information (willingness-to-accept). We conducted the study in two parts. In the first part we presented the study participants with two single closed-ended yes-no questions, asking them to protect themselves against information release ...

Estimated Protection Too Conservative

Jennifer Vines This is the fi rst of 12 Correspondence pieces in this issue that are in response to an important PLoS Medicine Research Article: Auvert B, Taljaard D, Lagarde E, Sobngwi-Tambekou J, Sitta R, et al. (2005) Randomized, controlled intervention trial of male circumcision for reduction of HIV infection risk: The ANRS 1265 trial. PLoS Med 2(11): e298. Readers wishing to add their own ...

Personal medical information: Privacy or personal data protection? A theoretical approach to understanding the Canadian environment

Protection or Privacy? Data Mining and Personal Data

A multiclass classification method based on output design p. 15 Regularized semi-supervised classification on manifold p. 20 Similarity-based sparse feature extraction using local manifold learning p. 30 Generalized conditional entropy and a metric splitting criterion for decision trees p. 35 RNBL-MN : a recursive naive Bayes learner for sequence classification p. 45 TRIPPER : rule learning usi...