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Extracting Secrets from Machine Learning Systems

This is fascinating research about how the underlying training data for a machine-learning system can be inadvertently exposed. Basically, if a machine-learning system trains on a dataset that contains secret information, in some cases an attacker can query the system to extract that secret information. My guess is that there is a lot more research to be done here.

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Tracking People Without GPS

Interesting research:

The trick in accurately tracking a person with this method is finding out what kind of activity they’re performing. Whether they’re walking, driving a car, or riding in a train or airplane, it’s pretty easy to figure out when you know what you’re looking for.

The sensors can determine how fast a person is traveling and what kind of movements they make. Moving at a slow pace in one direction indicates walking. Going a little bit quicker but turning at 90-degree angles means driving. Faster yet, we’re in train or airplane territory. Those are easy to figure out based on speed and air pressure.

After the app determines what you’re doing, it uses the information it collects from the sensors. The accelerometer relays your speed, the magnetometer tells your relation to true north, and the barometer offers up the air pressure around you and compares it to publicly available information. It checks in with The Weather Channel to compare air pressure data from the barometer to determine how far above sea level you are. Google Maps and data offered by the US Geological Survey Maps provide incredibly detailed elevation readings.

Once it has gathered all of this information and determined the mode of transportation you’re currently taking, it can then begin to narrow down where you are. For flights, four algorithms begin to estimate the target’s location and narrows down the possibilities until its error rate hits zero.

If you’re driving, it can be even easier. The app knows the time zone you’re in based on the information your phone has provided to it. It then accesses information from your barometer and magnetometer and compares it to information from publicly available maps and weather reports. After that, it keeps track of the turns you make. With each turn, the possible locations whittle down until it pinpoints exactly where you are.

To demonstrate how accurate it is, researchers did a test run in Philadelphia. It only took 12 turns before the app knew exactly where the car was.

This is a good example of how powerful synthesizing information from disparate data sources can be. We spend too much time worried about individual data collection systems, and not enough about analysis techniques of those systems.

Research paper.

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Roombas will Spy on You

The company that sells the Roomba autonomous vacuum wants to sell the data about your home that it collects.

Some questions:

What happens if a Roomba user consents to the data collection and later sells his or her home — especially furnished — and now the buyers of the data have a map of a home that belongs to someone who didn’t consent, Mr. Gidari asked. How long is the data kept? If the house burns down, can the insurance company obtain the data and use it to identify possible causes? Can the police use it after a robbery?

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Corporations Misusing Our Data

In the Internet age, we have no choice but to entrust our data with private companies: e-mail providers, service providers, retailers, and so on.

We realize that this data is at risk from hackers. But there’s another risk as well: the employees of the companies who are holding our data for us.

In the early years of Facebook, employees had a master password that enabled them to view anything they wanted in any account. NSA employees occasionally snoop on their friends and partners. The agency even has a name for it: LOVEINT. And well before the Internet, people with access to police or medical records occasionally used that power to look up either famous people or people they knew.

The latest company accused of allowing this sort of thing is Uber, the Internet car-ride service. The company is under investigation for spying on riders without their permission. Called the “god view,” some Uber employees are able to see who is using the service and where they’re going — and used this at least once in 2011 as a party trick to show off the service. A senior executive also suggested the company should hire people to dig up dirt on their critics, making their database of people’s rides even more “useful.”

None of us wants to be stalked — whether it’s from looking at our location data, our medical data, our emails and texts, or anything else — by friends or strangers who have access due to their jobs. Unfortunately, there are few rules protecting us.

Government employees are prohibited from looking at our data, although none of the NSA LOVEINT creeps were ever prosecuted. The HIPAA law protects the privacy of our medical records, but we have nothing to protect most of our other information.

Your Facebook and Uber data are only protected by company culture. There’s nothing in their license agreements that you clicked “agree” to but didn’t read that prevents those companies from violating your privacy.

This needs to change. Corporate databases containing our data should be secured from everyone who doesn’t need access for their work. Voyeurs who peek at our data without a legitimate reason should be punished.

There are audit technologies that can detect this sort of thing, and they should be required. As long as we have to give our data to companies and government agencies, we need assurances that our privacy will be protected.

This essay previously appeared on CNN.com.

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