Devices that make up the Internet of Things (IoT) collect a monumental amount of data about their owners. In most cases, the data they gather benefits the owner of the device and performs some useful purpose for them. However, when viewed in aggregate, the data gathered can reveal an enormous amount of information about the devices’ owner that can be very invasive if this information were to fall into the wrong hands.
Over the course of several months, Charles Givre did an experiment in which he collected data from several IoT devices including a Nest Thermostat, the Automatic Car dongle, the Wink hub, and a few others in order to determine what could be learned about the owner of the devices. Givre approached this experiment like a law enforcement or intelligence investigation, beginning with a bit of seed knowledge about the target, and built a profile about the target using the data that was available via these devices’ APIs and the data they transmit over the internet.
This presentation is not about how to bypass the devices’ security features, hack them, or how to mess with people by randomly turning off their A/C; but rather focuses on the consequences of IoT devices collecting and storing data.
Charles Givre is an unapologetic data geek who is passionate about helping others learn about data science and become passionate about it themselves. For the last five years, Charles has worked as a data scientist at Booz Allen Hamilton for various government clients and has done some really neat data science work along the way, hopefully saving US taxpayers some money. Most of his work has been in developing meaningful metrics to assess how well the workforce is performing. For the last two years, Charles has been part of the management team for one of Booze Allen Hamilton’s largest analytic contracts, where he was tasked with increasing the amount of data science on the contract—both in terms of tasks and people.
Even more than the data science work, Charles loves learning about and teaching new technologies and techniques. He has been instrumental in bringing Python scripting to both his government clients and the analytic workforce and has developed a 40-hour Introduction to Analytic Scripting class for that purpose. Additionally, Charles has developed a 60-hour Fundamentals of Data Science class, which he has taught to Booz Allen staff, government civilians, and US military personnel around the world. Charles has a master’s degree from Brandeis University, two bachelor’s degrees from the University of Arizona, and various IT security certifications. In his nonexistent spare time, he plays trombone, spends time with his family, and works on restoring British sports cars.
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