Melissa Tobin and I discuss the prediction of crime using twitter data and the role this data could play in police planning, public policy, and public service delivery.
My discussion with @CBCEarlyEdition regarding Tinder, the evolution of spam, and finding love via video games
My discussion with @lorenmcginnis on the culture of video games, esports, Twitch.tv, athletics, trolling, and why Microsoft will be introducing a community powered reputation system for Xbox One.
Nitesh Dhanjani: Cursory Evaluation of the Tesla Model S: We Can't Protect Our Cars Like We Protect Our Workstations
A fantastic article exploring significant security concerns with the Tesla Model S automobile, while also touching upon the need to consider security and privacy with regard to the rise of the Internet of Things.
Given the fantastic future of IoT (Internet of Things) devices ahead of us, it is the responsibility of the security community and device manufacturers to do our best to enable these devices securely. The IoT devices in scope include remotely controllable thermostats, baby monitors, light bulbs, door locks, cars, and many more. The impact of security vulnerabilities targeting such devices can lead be physical in nature in addition to contributing to loss of privacy.
The purpose of this document is to outline the mechanisms by which the Tesla Model S communicates with car owners and the Tesla infrastructure using a variety of TCP/IP mechanisms. The goal of this document is to advise the owners on security issues they should be aware of as well as to kick off a dialogue between security researchers and Tesla Motors that will ultimately drive deeper analysis and assurance.
A steel cable from the iron collar around the condemned man’s neck runs up to a pulley and back down to a spool connected to the back of an old bubblegum machine.
His cell, open to the city’s elements and bustling streets, sits next to the coin-operated spool.
A quarter [dollar] turns [the spool] a quarter [of a] turn. But istead of dispensing bubblegum, it dispenses justice by winding up the wire like a winch.
A half dollar for a half turn. A dollar a turn.
One good turn deserves another, so people line up at the machine to take turns buying turns.
This is the old model. Gallows 2.0, out next quarter, accepts Visa, Mastercard, and Bitcoin, and you can buy forward OR BACKWARD turns (to loosen the noose) while viewing through the cell’s webcam over the Internet.
Welcome to Justice 2.0
April 1, 2014
Micheal Dunn, Program Manager on Xbox Live, reviews details of the Xbox One reputation system.
A Review of the Data Broker Industry: Collection, Use, and Sale of Consumer Data for Marketing Purposes
The United States Senate Committee on Commerce, Science, and Transportation’s inquiry sought answers to four basic questions:
- What data about consumers does the data broker industry collect?
- How specific is this data?
- How does the data broker industry obtain consumer data?
- Who buys this data and how is it used?
Based on review of the company responses and other publicly available information, this Committee Majority staff report finds:
- Data brokers collect a huge volume of detailed information on hundreds of millions of consumers.
- Data brokers sell products that identify financially vulnerable consumers.
- Data broker products provide information about consumer offline behavior to tailor online outreach by marketers.
- Data brokers operate behind a veil of secrecy.
My discussion with Stephen Quinn on CBC Radio Vancouver’s Early Edition regarding efforts by the California Department of Motor Vehicles to regulate self-driving vehicles by 2015.
Self-driving cars sound like fantasy to many, but regulators are laying the groundwork for the technology to hit the roads next year.
Research by Matthew S. Gerber:
Twitter is used extensively in the United States as well as globally, creating many opportunities to augment decision support systems with Twitter-driven predictive analytics. Twitter is an ideal data source for decision support: its users, who number in the millions, publicly discuss events, emotions, and innumerable other topics; its content is authored and distributed in real time at no charge; and individual messages (also known as tweets) are often tagged with precise spatial and temporal coordinates. This article presents research investigating the use of spatiotemporally tagged tweets for crime prediction. We use Twitter-specific linguistic analysis and statistical topic modeling to automatically identify discussion topics across a major city in the United States. We then incorporate these topics into a crime prediction model and show that, for 19 of the 25 crime types we studied, the addition of Twitter data improves crime prediction performance versus a standard approach based on kernel density estimation. We identify a number of performance bottlenecks that could impact the use of Twitter in an actual decision support system. We also point out important areas of future work for this research, including deeper semantic analysis of message content, temporal modeling, and incorporation of auxiliary data sources. This research has implications specifically for criminal justice decision makers in charge of resource allocation for crime prevention. More generally, this research has implications for decision makers concerned with geographic spaces occupied by Twitter-using individuals.
LibraryBox is an open source, portable digital file distribution tool based on inexpensive hardware that enables delivery of educational, healthcare, and other vital information to individuals off the grid.
Discussing the NSA TURBINE initiative with Rick Cluff on CBC Radio Vancouver’s Early Edition