Hackers Can Track Subway Riders' Movements By Smartphone Accelerometer
Patrick O'Neill writes: Tens of millions of daily subway riders around the world can be tracked through their smartphones by a new attack, according to research from China's Nanjing University. The new attack even works underground and doesn't utilize GPS or cell networks. Instead, the attacker steals data from a phone's accelerometer. Because each subway in the world has a unique movement fingerprint, the phone's motion sensor can give away a person's daily movements with up to 92% accuracy.
Now if there were any subways anywhere near where I lived.
If the accelerometer has such poor security, what other components/sensors are vulnerable?
Everyone just needs to pool their phones and then everyone use a random phone for the day. Sort of a TOR operating at the physical level. An app that made encrypted VoIP calls could probably allow you to even use the same phone number by just logging in through the random phone of the day.
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If a hacker has access to accelerometer data, he/she probably has access to lots of OTHER personal info also.
Table-ized A.I.
The very premise, prior to the attack, is that the user has opted to run the "hacker"'s malware.
All they're saying, is that if run malware which watches the accelerometer, the malware can infer your location. And then it still has to transmit this information from your computer to another (unless the malware itself, is what make decisions based on your position).
Tin foil hat, now tin foil pocket.
The privacy concerns are troubling, but I can't help thinking that's pretty cool.
Because 0% accuracy is also "Up To 92%" accuracy.
Here in Melbourne, Australia our train system has a unique movement footprint.
Accellerating and breaking for no reason, trains that skip stations or terminate at random ones; this baby's got it all. Good luck decoding the position from that.
iOS and presumably other platforms use the accellerometer & gyroscopes for purposes like this and to provide inertial navigation. Its quite accurate at locating you in a subway. I catch the train home a few times a week and its really quite remarkable.
To do signature matching of accelleration/decelleration patterns at specific stations would require low level access to the accelerometer data, or to bypass user consent on location services (on iOS)
I'm not sure on Android, but on WinMo and iOS you'd need to be jailbroken for this attack to work. (there is no low level API available unless you are in a rooted/jailbroken state).
Its a cool hack, but the preconditions for it being used as a surveillance mechanism are very significant compromises.
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Why would law enforcement want to do this?
They can just get your location from your cell carrier.
Nah, you're missing the point. Starts and stops will have 'fingerprints', spacing between stops will show up, things like that. Kinda cool research.
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Who cares about this? Simply tracking which WiFi station IDs the phone sees is a lot better way of tracking where the person is.
If you can hack into their phone, you can find them. No need for fancy long-term acceleration tracking either.
http://lkml.org/lkml/2005/8/20/95
In the late 1970s in junior high we would ride the bus and get off at random stops and write down pay phone numbers. Then when we got home we would call the numbers and do all sorts of gags.
The one that inexplicably worked well was telling people that had won money from a radio station. Why they believed that an 8th grader sounded like a disk jockey is still beyond me.
It's almost kind of sad that kids of today can't get that experience. There's very few pay phones left and I bet none of them accept incoming calls. It was also pretty safe from a get in trouble perspective. Call logging and tracing would have been a huge endeavor and we never called any one pay phone more than a few times or suggested anything violent or even all that ribald.
Mind you I would have thought that on a train you could triangulate with mobile repeaters and such much more easily,
Not underground, where cell service is blocked by a hundred feet of rock and dirt.
John
The second whoosh is not transmitting accelerometer data, so he doesn't know where it is.
As soon as I saw the summary, I wondered how they're able to do decent dead reckoning using the mediocre quality cell phone accelerometers; in the general case, the integration would give drift pretty quickly. We're not dealing with ICBM-quality accelerometers here. So the interesting bit is how they're able to make use of information that specializes the problem (the location of subway stations) together with machine learning to do much better than the general case. The paper is worth a read.
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Sorry, but who comes up with this shit? Apart from not knowing the start location and orientation of the phone, electric trains are all pretty similar these days and besides which how will they take account of non station stops at reds, bad riding suspension on certain trains, fast/slow drivers etc etc?
What a crock of ....
Apart from that the accelerometers on your average consumer device arn't even that accurate. After a few minutes it'll be hopelessly lost.
For a municipal transportation chief?