Privacy is an increasingly important subject for organizations. Nowadays, organizations (unknowingly) process vast amounts of personal data of their customers in numerous different information systems. There are strict legislations regarding the processing of personal data, and from mid-2016, these legislations will only get more strict with the introduction of the General Data Protection Regulation in the European Union. In an approach to ensure compliance with these legislations, different techniques such Privacy Enhancing Technologies, Privacy-by-Design and Privacy Design Strategies were introduced in the past decades. However, these techniques tend to be defined in such a high-level of abstraction that they are hard to use in practice. This paper discusses and explains various software techniques which can help to design information systems that can better protect the privacy of their users. Next, these techniques are combined as a solution named Privacy Management System. This system is able to ensure and enforce full data processing transparency of an organization and should close the gap between the privacy legislations and software development.
This is a template for keeping a digital Research Diary in LaTeX, with an example entry and placeholder for your university / institution logo.
Original version by Mikhail Klassen who describes his experiences using LaTeX for research note taking in this blog post.
The goal of this project is to explore both the theory behind the Extended Kalman Filter and the way it was used to localize a four-wheeled mobile-robot. This can be achieved by estimating in real-time the pose of the robot, while using a pre-acquired map through Laser Range Finder (LRF). The LRF is used to scan the environment, which is represented through line segments. Through a prediction step, the robot simulates its kinematic model to predict his current position. In order to minimize the difference between the matched lines from the global and local maps, a update step is implemented. It should be noted that every measurement has associated uncertainty that needs to be taken into account when performing each step of the Extended Kalman Filter. These uncertainties, or noise, are described by covariance matrices that play a very important role in the algorithm. Since we are dealing with an indoor structured environment, mainly composed by walls and straight-edged objects, the line segment representation of the maps was the chosen method to approach the problem.