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Tracking Human Mobility using WiFi signals
Denmark Created: 5 Nov 2015
When I started working on understanding social systems, privacy really wasn’t on my mind - But one of the central realizations arising from our SensibleDTU experiment is that privacy needs to be an important part of this kind of research.

I’ve written about this at length elsewhere. One of the things we noticed while digging into terabytes of social data is that data-channels are highly correlated. Information “bleeds through” … something which has serious implications for privacy. Case in point: My group has just released a new preprint (get it here) that shows how the WiFi information routinely collected by your smartphone can easily be converted to precise information about your location. WiFi routers reveal where you live, work, and spend your leisure time. While your phone may have told you that WiFi helps “improve location accuracy”, it may come as a surprise that:

- A majority of apps in the store have access to the list of routers around you (scanned every 20 seconds).
- Your Android smartphone by default scans for WiFi routers even if you disable WiFi.

Our research shows

- How to easily convert WiFi information into geographical position.

- That although it sounds like all WiFi scans might be a lot of data to process, your mobility can be described using just a few of access points. And we have built an Android app which only requires WiFi datato illustrate how this works for your own mobility.

-That if someone knows these routers at some point in time, they will still know a lot about your mobility six months later.

Thus, while WiFi networks are intended for enabling connectivity, they are also a de facto location tracking infrastructure. More generally, our world is becoming more enclosed in a web infrastructures supporting communication, mobility, payments, and advertising. Logs from mobile phone networks (call detail records, CDRs) constitute a global database of human mobility and communication networks. Credit card records form high-resolution traces of our spending behaviours.

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Source: IDA Universe, Sune Lehmann & Piotr Sapieżyńsk, 30 Oct 2015

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