More than 150 leading global health and development thinkers convened on June 15, 2016, for The Innovation Effect: Powering Disruptive Global Health Solutions in Washington, DC. This conference report provides an overview of the insights shared when attendees explored what happens when unique partnerships, disruptive technologies, transformed systems, and data-driven insights combine in often unexpected ways to create dramatic improvements in the health and well-being of people around the world.
As VLSI technology has been improved, a smart card employing 32-bit processors has been released, and more personal information such as medical, financial data can be stored in the card. Thus, it becomes important to protect personal information stored in the card. Verification of the card holder's identity using a fingerprint has advantages over the present practices of Personal Identification Numbers (PINs) and passwords. However, the computational workload of fingerprint verification is much heavier than that of the typical PIN-based solution. In this paper, we consider three strategies to implement fingerprint verification in a smart card environment and how to distribute the modules of fingerprint verification between the smart card and the card reader. We first evaluate the number of instructions of each step of a typical fingerprint verification algorithm, and estimate the execution time of several cryptographic algorithms to guarantee the security/privacy of the fingerprint data transmitted in the smart card with the client-server environment. Based on the evaluation results, we analyze each scenario with respect to the security level and the real-time execution requirements in order to implement fingerprint verification in the smart card with the client-server environment.
Integrating the Healthcare Enterprise (IHE) has put together a handbook on patient de-identification. This handbook explains the process for removing individually identifiable information from healthcare data. This includes de-identification, pseudonymization, re-linking, design consideration, techniques, and risks. The intended audience is IHE Profile editors and healthcare information technology implementers needing a guide for designing and implementing de-identification systems.
