The Strata + Hadoop World in Barcelona 2014 Complete Video Compilation is also available at O’Reilly’s – and highly recommended.

Here are my slides:

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Location

When we analyze the raw data, that we collect through our mobile sensor framework ‘explore’ we found several other fingerprint-like traces, that all of us continuously drop by using our smartphones. Obviously we can reproduce de Monjoye’s experiment with much more granular resolution when we use the phone’s own location tracking data instead of the rather coarse grid of the cells. GPS and mobile positioning spot us with high precision.

Wifi

Inside buildings we have the Wifis in reception. Each Wifi has a unique identifier, the BSSID and provides lots of other useful information.

Wifis in reception around my office. When the location of the wifi emitter is known we can use signal strength to locate users within buildings.

Even the aribrary lable “SSID” can often be telling: You can immediately see what kind of printer I use.

Magnetic fields

To provide compass functionality, most smartphones carry a magnetic flux sensor. This probe monitors the surrounding magnetic fields in all three dimensions.

Each location has its very own magnetic signature. Also many things we do leave telling magnetic traces – like driving a car or riding on a train. In this diagram you see my magnetic readings. You can immediately detect when I was home or when I was traveling.

Battery

The way we use the phone has effect on the power consumptions. This can be monitored via the battery charge probe:

The battery drain and charge pattern is very unique and also telling the story of our daily lives.

Hardware artifacts

All the sensors in our phones have typical and very unique inaccuracies. In the gyroscope data shown at the top of the page, you see spikes that shoot out from the average pattern quite regularily. Such artifacts caused by small hardware defects are specific to a single phone and can easily be used to re-identify a phone.

No technical security

“We no longer live in a world where technology allows us to separate communications we want to protect from communications we want to exploit. Assume that anything we learn about what the NSA does today is a preview of what cybercriminals are going to do in six months to two years.”
Bruce Schneier, “NSA Hacking of Cell Phone Networks”

As Bruce Schneier points out in his post: there are more than enough hints that we should not regard our phones as private. Not only have we learned how corrosive governmental surveillance has been for a long time, there are lots of commercial offerings to breach the privacy of our communication and also tap into the other, even more telling data.

But what to do? We can’t just opt-out. For most people, not using mobile phones is not an option. And frankly: I don’t want to quit my mobile. So how should we deal with it? Well, for people like me – white, privileged, supported by a legal system providing me civil rights protection, that is more discomfort than a real threat. But for everyone else, people that can not be confident in the system to protect them, the situation is truly grim.

First, we have to show people what the data does tell about them. We have to make people understand what is happening; because most people don’t. I am frequently baffled how naive even data experts often are.

Second, as Bruce Schneier argues, we have to get NSA and other governmental agencies to use their knowledge to protect us, to patch security breaches, rather then exploit these for spying.

Third, it is more important then ever, to work and fight for a just society with very general protection of not only civil but also human rights. Adelante!

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Another example is given by A. Overeem et.al. who track urban temperature over time in various metropole regions arround the globe. The approach is as simple as powerful: a regression over the battery temperature (that is measured by every smartphone anyway).

The microphone, too, can give valuable data on local environmental conditions for an unlimited mass of individual users that participate. Sound level show noise emmission that can be located in space and time. Noise is regarded as a prime source of stress, but rather little is known about the changes that occur in different microgeographic regions.

Apps like Weather Signal use thus a combination of the phone’s sensors to contribute to a richer model for weather conditions.

Appart from just passivly deploying the phones as sensor boards themselves, it is of course also possible to collect data from other local sources and just transmit the results via smartphone. This can be done by letting the users take a picture of some reading of a scale which can then be processed via image recognition. Or you just ask people to put in the readings or their observations into some kind of questionnaire.

The fascinating thing is: since so many people in almost every country carry a smartphone, monitoring environmental conditions and changes is now possible on far larger scales than ever before.

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Smartphones carry versatile sensors. With appropriate apps, expensive instruments can be very well replaces – even sometimes the Geiger counter.

Gamma rays which are often products of radioactive decay, are also electromagnetic waves, just like light, however much more energetic. That means: as radioactive radiation can expose a chemical photographic film, it can as well effect the camera chip in the smartphone.

A team of researchers at the Idaho National Laboratory in Idaho Falls have used this property to change common smartphones into detectors for radioactive radiation. The radiation is recorded via the camera an an app, which calculates the radiation intensity from the data collected.

With this approach we learn again, how versatile mobile devices can be deployed. Up to thirty sensors in each smartphone measrure all kinds of variables like temperature, magnetism, brightness, sound and many more. With a little creativity we can combine these measurements and get valuable data about the environment around the smartphone and its user, that not rarely can replace expensive, specialized methods.

Here the link to the original publication:
Joshua J. Cogliati, Kurt W. Derr, Jayson Wharton: Using CMOS Sensors in a Cellphone for Gamma Detection and Classification

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