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It looks as if the teapot is responsive to my phone's gyros. I would've thought it would need special permission for that.

What a terribly clever Easter egg.




Nope. Actually, there was a paper published recently on using a phone's gyro / accelerometer (through the HTML5 APIs) in order to keylog users; because the API is precise enough for you to detect subtle motion of the phone as you press on the software keyboard.

Apparently in some mobile browsers, you can continue to poll the API even when your tab is not on focus.


Oh, that's really nice. So if you have a fix (and even if you don't) you can use dead reckoning and tie any points where the GPS is accessible and then re-create the path the phone took. That's a bit of a leak. Wonder how long after or before a GPS fix this would be effective, those phone accelerometers probably aren't all that accurate but you might be able to calibrate the one in a specific phone if you have data available for both of them for some stretch of trajectory.


Even using calibrated and temperature controlled consumer level accel/gyro sensors for dead reckoning results in estimated velocity error reaching few meters/s in a second or two.

Current consumer level sensor quality is enough for: a) attitude estimation; b) smooth interpolation between GPS updates if they arrive often enough;


IIRC the accelerometers are way to imprecise to correctly detect starts and stops. Errors compound so quickly that any kind of useful accuracy for dead reckoning is a long ways out.


For once a bad sensor is good news.


More proof that Javascript and HTML5 are cancer for privacy on the internet


link the paper please?


https://doi.org/10.1007/978-3-319-58460-7_15

Keystroke Inference Using Smartphone Kinematics

The use of smartphones is becoming ubiquitous in modern society, these very personal devices store large amounts of personal information and we use these devices to access everything from our bank to our social networks, we communicate using these devices in both open one-to-many communications and in more closed, private one-to-one communications. In this paper we have created a method to infer what is typed on a device purely from how the device moves in the user’s hand. With very small amounts of training data (less than the size of a tweet) we are able to predict the text typed on a device with accuracies of up to 90%. We found no effect on this accuracy from how fast users type, how comfortable they are using smartphone keyboards or how the device was held in the hand. It is trivial to create an application that can access the motion data of a phone whilst a user is engaged in other applications, the accessing of motion data does not require any permission to be granted by the user and hence represents a tangible threat to smartphone users.




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