 My name is Michael, but just you can just call me Michael. And well, I started the project with the name crowd flow.net and I will talk about it in a few seconds. Well, usually I make data visualization stuff for newspapers and websites and stuff like that. For example, this was the last project I visualized some data from from Facebook. Is this 1629? It looks a little strange. It doesn't matter. Okay, so Facebook data from MaxTremps. You can see it at Facebook versus Europe. We did it for Tuts. And probably the most famous project was the data retention visualization on site online. I did it with Lorenz Matzart and we visualized the telecommunication provider data from a guy called Malta Spitz. And it's really interesting to analyze it, but it would be more interesting to analyze the whole society if you can see the pattern of the whole society. Because this location gate thingy, you probably remember the iPhone tracker that you can install a small application that extracts the location data from your iPhone and presented it in a map. And you can see how you move through Germany. And basically it's working that the iPhones are scanning Wi-Fi stations nearby measuring signal strength and stuff like that and sending it to Apple. And Apple generates a map of Wi-Fi stations all over the world. And it then sends this data to iPhones and these iPhones using the location of the Wi-Fi station to triangulate their own position. So it's basically like GPS, what it's called WPS. It's a Wi-Fi positioning system. And it's cheaper, easier, faster than GPS and using less battery and stuff like that. So we started this project CrowdFloat.net and basically it's a Java app that can extract the data, the location data from your iPhone. And you can see it for yourself. It's a simple CSV file, but you can also upload it to us. And currently we collected 1500, well, data donations. And we made some analysis and some visualizations out of that. The first one is where does all these stuff come from? So mostly it's from Europe, big part of the United States. Of course, Germany is pretty big because it started here in Berlin. And you can do a lot of stuff, a lot more stuff. For example, 150,000 Wi-Fi stations are here in Germany. And you can use the MAC address to find out who's the manufacturer of these Wi-Fi stations. And you can make a chart and you can see that AVM is the manufacturer of the FritzBox, the DSL Wi-Fi station. It's the most popular Arcadion Cisco D-Link Netgear. This is a map of Wi-Fi stations in Berlin. I don't know how much it is, but probably a million. I'm not sure. Currently we have 30 million Wi-Fi stations. You can download it. It's a 1.5 gigabyte file. You can download all the stuff on CrowdFloat.net. But today I want to show you a new bug we found in this... Oh, no, that's really great. It's a mesmerizing video I called Firefly. Because it looks like 900 fireflies moving through middle Europe in a time-lapse video. And it's an HD on YouTube. You should definitely see it. Great stuff. But now here's the actual bug I want to show you. This is a small town in Brandenburg. It's Neuropin. Probably never heard of it. And I added some dots for every Wi-Fi station in Neuropin, according to Apple where these Wi-Fi stations should be. And the interesting thing is that the Wi-Fi stations are distributed and somehow there are some apartments and some buildings in the States where they seem to have five or up to ten Wi-Fi stations. So why would somebody install ten Wi-Fi stations at home? And these small clusters seems only to be in villages with a lot of density of iPhone and Wi-Fi. So it seems to be a bug on Apple's side. So I investigated it in a village and I found out that these are the places where people live who have an iPhone. So why is that? Well, the reason is that the iPhone is collecting data about the Wi-Fi stations nearby and sends it to Apple. And Apple uses this data to triangulate these Wi-Fi stations to create this map. But if you have just one iPhone and you want to triangulate the Wi-Fi stations nearby, then if you just watch it from one point of view, you can't triangulate it. So the best estimate would be that all Wi-Fi stations are just very close together. So that means that at all places with just one iPhone, it started to, well, drag and pull all the Wi-Fi stations, well, at least the locations, to such small clusters. And, well, since Apple is still publishing the data, you can use this map to check if in a village is somebody living with an iPhone. Well, that's a funny thing because I don't think Apple actually thought about publishing a map of iPhone users. Well, doing this work, I had a very interesting question because every data that Apple publishes is anonymized. You don't know which iPhone you don't know, which Wi-Fi station it will, at least to whom it belongs, but still anonymized data still contains personal identifiable information. Because you then know which iPhone it was or which Wi-Fi station. But you can see that clusters are at the specific addresses, specific houses. Well, I think you should think about it, what data privacy and data protection really means and how you can enforce it on not. Thank you very much if you have questions.