A Data Transparency Framework for Mobile Apps

This is a paper that I drafted in my spare time that relates to the work I’ve done with mobile technology and law enforcement. I got to thinking that the method we were using to track information about the various stakeholders and how data are collected and shared could have wider applicability. I’ve submitted this to a journal, but until I hear back I will leave it here as a post.

Abstract— In today’s mobile application marketplace, the ability of consumers to make informed choices regarding their privacy is extremely limited. Consumers largely rely on privacy policies and app permission mechanisms, but these do an inadequate job of conveying how information will be collected, used, stored, and shared. Mobile application developers go largely unrewarded for making apps more privacy conscious as it is difficult to communicate these features to consumers while they are searching for a new app. This paper provides an overview of a framework designed to help consumers make informed choices, and an incentive mechanism to encourage app developers to implement it. This framework includes machine readable privacy policies encouraged by mobile app stores and enhanced by user software agents. Such a framework would provide the foundation required for more advanced forms of privacy management to develop.

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Derivatives in Apache Math Commons Post Version 3.0

For my dissertation, I’ve been using the Apache Math Commons Java library in order to incorporate some numerical analysis into an agent based economic model I’ve built. I started out using version 3.0 of the library and defining a function that had a derivative was pretty straight forward.

When I migrated to version 3.2, one thing that took me a little while to figure out was how to use the new DerivativeStructure. I thought I would give a very simple example to help others out. Continue reading

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Visualization of U.S. Electricity Generation via the EPA’s eGRID Dataset

I’ve been using the data visualization software package called Tableau for a while now. It is really a fantastic product, with lots of online tutorials and help. It even has a free one-year student edition! Below is my attempt to show how power is generated in the United States. The data comes from the EPA’s eGRID data set, with 2009 being the most recent data. It is a geographic bubble chart with the amount of energy produced at each plant represented by its size, and the fuel source for that energy by the bubble’s color. I’ve used some transparency to help with the overplotting. Even so, many points close together are hard to distinguish. I added a dark border to each bubble to help with that.

eGRID Annual Net Generation Data by Fuel Type

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