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Metadata Investigation

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Hey landmark only a a b was a
big
so we have some kind of release small strange bit unofficial led and a group of people that they're working together with the 1 sided frenzied we have a some people who are like in 2 on text research legally searches in the courts have some kind of strange by grounding in Media Teri not and what we like to do we like to investigate different kind of invisible things and basically we started with like a really simple investigations like power networks looks like our surroundings looks like and it basically we be making some kind of like a data visualization of of different kind of
natural topologists then we're mapping
all of those websites with the disease then what what's the mn traces the the uh leading to others when we are like visiting those websites so we're able to trace the for example when we are typing pattern on www facebook . com where those packets of traveling from and to detect different forms of like some kind of Internet harbor surround and then we said
OK let's try to to do a bit deeper investigation so we were basically uh exploring inside of the websites what of some kind of 3 3rd party uh embedded in those websites and where all those data is going do so we came up with this kind
of visualizations so we basically
get some results and we saw that flight Boo Boo it's collecting all those data Facebook and
so on but this is the most what all of you know already know but then we relate mostly interested in
analyzing individual companies because we said OK it's OQ which this kind of of surveillance economy that we really want to understand how this is functioning so this is for example will how he's like extracting
information about our visits from different types of and we did the same to the Facebook the and then we said OK of course this is
just the the the lethal segment of all these surveillance uh economy there probably really really scary thing is happening on the mobile phone so we met all the permissions and this kind of an optical structure it's like vizualization for open so that you're giving to a different set
of and more while applications so for example this is Facebook and then they're collecting different kind of information so for example fault also media files on so on if you
will like the deeper you will understand that they're are able to access most of mostly anything that exist on your mobile phone and then they were not
investigating further in in in Serbia like OK but how different government agencies are basically collecting information about retaining data so we were like mapping all of those structures based on some kind of 2 thousand pages of documentation that we're able to together on different ways so step that we will be really understanding how the and how like scary this kind of surveillance economy yes but
we just in the like prior investigations were able to understand how they're collecting our data but we never were able to understand what they're basically doing with which with this data and then what happened it's
that there was like 1 really nice moments so July 2015 rose 1 really big league of e-mails from company called 2nd team and basically some kind of 400 gigabytes of data their internal e-mails was there a thanks to 2 some people and then published on on on on Wikileaks and on other
side we have another really coming of course from Snowden so we have some kind of picture what for example and say what kind of methodology and say doing in analysis of metadata so we said talking on 1 side we have some kind of methodology but on another side we finally have our own be the data so let's cross those things together so what we did we basically tried to do the same thing that Tennessee's doing tossed on a daily base but to do this to someone else that basically to hocking thing with hacking thing if you those and also this is 1 of the biggest uh companies in the world that is like a some kind of cyber veteran on manufacturing and distribution so they're selling different kind of tools
for government agency and probably others to get into your your e-mail accounts more well formed
so Facebook chats or whatever you can think of so with that OK let's investigate their so every e-mail have had there and have a
constant and for us because you want to just to use like metadata analysis we set up just concentrate on the cat we don't care what is in the content and our database was like really simple subject dates and from and I others of descendants so what the 1st beat me to do some kind of social network analysis and by doing this you get some
kind of structural like this and so you can investigate the big adults that means that this person is sending a lot of e-mails that external contacts ensemble of this looks a bit bit messy the and then basically started do different things to play with the data so we feel the only people who and more than 100 e-mails so we get basically a structure of the organization because those nets those daughter this people inside of the shocking thing then we played with the same dataset on on on the way so we really on the side you have people who cracking thing on this other side as well so the dark adults means that those 2 persons are communicating a lot between each other so from here where we were able to understand some utterances who is the main guy who is communicating which who how how often and so on and then if you like like to respond the same picture into another graph you basically got some kind of organizational structure of the shocking thing that for us that was fun
and so you see the main guy this data regions that he some kind of control 3 His communicating with everyone sending a lot of e-mails that you see another people people who is communicating which who so we are on the good stuff that but what we can do we can add also time complement and see for each of them when he is active and how often he's he's sending e-mails then of course because you're like really curious we're
interested about external contacts and just and just with a few clicks you were able to visualize that who are the the most frequent external contacts of the organization this led
us to investigate it more than the and so on then we were able to cross people from cracking theme with external contact and then to understand what are the patterns of communication between them then we set up a just let's analyze now just the the domain names so this is for example of of all external contacts but the grouped into different categories so those are another money if Beckham manufacturers or like a different kind of cocking the organization of those are uh states a security organization and all those of some kind of investment groups loyalists so we get like a picture overview how this industry look like and awarded the main players so and then we were able to
follow like different companies so for example this is nice about on and every colour
represent another person so we can see here that like then people from this organization is communicating with 10 people from talking thing so they're like really tied together and and then we said OK we never heard for this company and I look
upon and basically this company is doing the same thing selling the same almost the
same product but not to governments to different companies around the world toward surveilling the workers for them so and here for example if we're looking more of this kind of graphs we can see for example coherent the guy from probably see all this company its 1st doing some and then he's going out from communication another person is centering genes so we're able to understand the dynamics of how they communicate with the different companies and and what's going on then we said OK now we're going to more data has said OK let's follow the person that let's follow the guy so what we have here it's something that's called bottom of life analysis and some kind of military vocabulary but basically they're doing that so if you're analyzing just send the e-mail sort of sent e-mails sent e-mails it's how you it basically really really
personal information because it's how you are reacting to things so so we can see that this guy David he's like making up almost every day really early around 4 o clock that having some POS around seeks then going probably all doing some jogging then going on his job than working a lot around 11 that having a pose for lunch around to obtain and mostly not working anything around of after 24 on the other side the flip the fleet underlies the e-mails that he's receiving that's a pattern of the surrounding that support and of how his peers are are basically function and we can see that there be different you know they just want to job and then you know have some peak around 10 bucks then launching
slowly not working a lot of that's what people do not but then we were able to follow David's like what is left his
behavior during the week or during the year and every changing that bottom it can lead to something because the number of female that
you're sending if he can tell are you depressed or you seek were fell in law or something else so those changes means a lot of you know when see some drop it means that something is happening to David and then we play that this is going into some kind of a beta musturbation that we then we tried to understand the other partons and if you visualize them like this you can see some people and those are the dates when something happened to David all something is strange because he is like sending i don't 20 miles per hour or something the and this is my favorite 1 such having done more this queries more e-mails those there's a that those amounts they so you here you can really have really have a some kind of visual background to conciliate like different part so for example this 1 you have emptiness will and then you have something like a switching switching 1 side and then you have emptiness again what's that and then we investigate this strange but and we re and re learn that this is the moment when he moved to proceed approach so that
means traveling time the no connection and then change of time zone and then traveling back then we this pattern it's
the similar but is going in another that action this is when he went to New York different time zone this a shorter flight and this 1 of the favorite 1 this is the moment this is some mass here this is the moment when citizens were published research about shocking thing it so you can see the you can see the drama all
In those pixels then in subject line you can find a lot of interesting things so every time when you're ordering something from a muslim in subject line it's
written in the name of the product so we were able to understand and to know all the equipment that they buy over a Muslim and who in which bond so you have like different movies you have different kind of recruitment poster mobile phones and things for this but then another really interesting think so they have a company that is like providing them up tickets for flights the and also in subjects it's freedom the name of the airports in the name of the person so from that we were able to basically extracted for each of them how many times they travel and we're so we get like 1st all their movements then we're able to 2 separated the some different people so we understood which member is coloring which parts of the world yeah and then if we compare all of this together we will be able to understand where they're meeting each other so for example meeting in Mexico baroque all different countries and quiz meeting cool and you understand if if there's like 4 5 of them meeting in 1 place that means that they're doing something but also the time or they have a
fun but even more moral
the aggressive think it's basic he then in in IP addresses of the senders because every time when someone is sending an e-mail to David he's reviewing his IP address in most of the time of evidence so we were able to track every each person will send an e-mail to David exact location and and for example this adjust analysis of their movements so you can see like these guys and these guys all the time and single board another 1 is moving with these so lots of interesting information the and then when we analyze external context we're able to come up to them like on the precision of the see the all of them so those are some kind of strange people but we were able to able to go even deeper so for example this is a long so all this we know that the guy from the UK government with his name and surname was in that moment you're communicating with talking things so the that the trees cadastre
and then did the emotionally for us it was also like you
like in not so nice feeling to do this because in 1 moment we understood we're going to deep into someone authors at another side this is what is happening to us on the daily basis in real time well by different actors so who want who can do this will look into this they can do this like plot of different factors all the goal of them who have access to to your e-mails can do the same thing and what is really scary is that this is happening in real time and in most of the case it's it's run by the algorithm score then able to understand what your particles in the same way I understood what is the pattern of David so they can
our the system if you move somewhere if you start to behave differently than usual if you change your pattern of of life and then this lead us to some kind of predicting the future behavior when our partons otherwise than than so song that lead us to some kind of speech I'm pretty coke and Altaf other really really scary senators the the so that that was our investigation of talking
thing next investigation title so easily the tree of doing it is about Facebook but it's
not so the so via mapping gold that the inputs interphase look like for example
this is when you like when you share when added to the and how those different kind of kinds of information there are being stored inside Facebook how would it fuck factory basically we are doing this by reading some kind of 8 thousand different Facebook apartments not doctors but often and trying to understand what they exactly do with all of our our data and how different algorithms are basically transforming these our behavior into the product on the right and repeated in that you cannot see so well it was really cycle graphs so that's my story about talking to the mandate of his isation is there an image questions about it but not microphones coming 1 but I
have 2 questions the 1st question of whether any moral problems to you you rules using the real name of this chef of equity and the 2nd question is
um do you think there's an nests and necessity and for all humans get involved in analyzing this data on what is called a be done by computers this of at the 1st 1st thing
so we prefer thinking tool to change the names of the people with different animals and that was like really funny and that the baby's usable for or something like this in in order because we have like 1 legal team in our organization so we had like a really long discussion over month about different at the call ego genes and at the end they said knowledge they
but on 1 side and all of those data it's public on other side so we didn't like X. poles it's actually something if someone it's like a pooling their names which will appear some of those of names and their returning like we can leak WikiLeaks database and so on so at the end
I just chose to to publish this like this and told I don't know what I mean like really really put out in some way it's that this kind of this hour investigation of all of the fucking theme became some kind of case starving in this data analytics society so I've written when you when you will like metadata investigation you have 15 some kind of psycho being in big of if I'm proud and on that also but what was the other which is the human send and the you know
the it's completely done very much of it in this case is done by humans have but but it's done in but I believe in all other cases is bound by machines and this is why metadata it's so important as this node instead we don't care about content so much in the In an emissary care about metadata because metadata can be analyzed by software so and soft and and content can lie in different ways but metadata it's like metadata in a lot of people asking do you need to you know like because it's kind of investigative journalist journalism and and like if you're doing investigating journalism usually have a different pools from
different sizes OK whether or how to collect post because like its metadata it's like a flight from I him to move another opinion so yes that the machines is basically in 99 per cent of the time this is done by machines and this is why it's so so this is why a the and this government agencies like so much metadata because it's really easy to process yeah OK we have 10 1 last question from I was just curious of to do this as a 4 comp across abrasive you substitute of yourself to machine as you just said In this process the stairs something that you think you heard gained
as an understanding of what I think he was doing our on level of most of mostly out that even the knowledge level about what was happening by looking at that they are show long time and where something essential roles level of knowledge that you were able to achieve through that up with your own human brain has something in that I think this can
be but if you know this is really powerful tool and and in so we really powerful knowledge and I was most of time out of events I'm approach by the people who are asked me like do you want to target our audience on this way and like so I I I learned a lot by doing that but did we discover something I think the the biggest discovery not it's not about taking
15 the biggest discovery it's how their metadata analysis it's intrusive and and by understanding that that was the the biggest shock that tree here because from just like 4 different columns of data we were able to reconstruct for lots of different things and that that's really that's really scary so I
think you have to look at how many of you in an
Neue Medien
Bit
Subtraktion
Metadaten
Datennetz
Gruppenkeim
Visualisierung
Neue Medien
Computeranimation
Leistung <Physik>
Mapping <Computergraphik>
Facebook
Web Site
Mustersprache
COM
Ablaufverfolgung
Mengentheoretische Topologie
Computeranimation
Internetworking
Bit
Web Site
Visualisierung
Web Site
Computeranimation
Resultante
Facebook
Besprechung/Interview
Facebook
Subtraktion
Menge
Offene Menge
Datentyp
Web Site
Information
Datenstruktur
Technische Optik
Computeranimation
Neue Medien
Facebook
Subtraktion
Besprechung/Interview
Kartesische Koordinaten
Information
Elektronische Publikation
Computeranimation
Subtraktion
Metadaten
Information
Datenstruktur
Analysis
Computeranimation
Homepage
Leck
Distributionstheorie
Internetworking
Subtraktion
Prozess <Physik>
Momentenproblem
Cybersex
Reverse Engineering
Computeranimation
Metadaten
E-Mail
Hacker
Stochastische Abhängigkeit
Analysis
Facebook
Chatten <Kommunikation>
Besprechung/Interview
E-Mail
Bit
Gewicht <Mathematik>
Graph
Content <Internet>
Selbst organisierendes System
Datenhaltung
Übergang
Telekommunikation
Frequenz
E-Mail
Knotenmenge
Analysis
Computeranimation
Datenhaltung
Graph
Metadaten
Datenstruktur
Datennetz
Gruppe <Mathematik>
Computerunterstützte Übersetzung
Datenstruktur
E-Mail
Innerer Punkt
Analysis
Selbst organisierendes System
Besprechung/Interview
Gamecontroller
E-Mail
Dialekt
Telekommunikation
Domain-Name
Kategorie <Mathematik>
Selbst organisierendes System
Zahlenbereich
Computersicherheit
Mustersprache
Gruppenkeim
E-Mail
Computeranimation
Domain-Name
Selbst organisierendes System
Besprechung/Interview
Telekommunikation
Computeranimation
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Prozess <Physik>
Besprechung/Interview
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Ungerichteter Graph
E-Mail
Computeranimation
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Mustersprache
E-Mail
Analysis
Virtuelle Adresse
Lineares Funktional
Transinformation
Diskretes System
Peer-to-Peer-Netz
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Quick-Sort
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Momentenproblem
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Gesetz <Physik>
Zeitzone
Analysis
Computeranimation
Sinusfunktion
Arithmetisches Mittel
Programmfehler
Zahlenbereich
Mereologie
Mustersprache
Mapping <Computergraphik>
Visualisierung
MIDI <Musikelektronik>
Tropfen
Ereignishorizont
Mustererkennung
Subtraktion
Pixel
Momentenproblem
Gruppenoperation
Ruhmasse
Zeitzone
Gerade
Computeranimation
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Verbandstheorie
Betragsfläche
Einheit <Mathematik>
Mereologie
Extrempunkt
E-Mail
Biprodukt
Gerade
Computeranimation
Momentenproblem
Information
E-Mail
Netzadresse
Computeranimation
Schlussregel
Netzwerktopologie
Einplatinen-Computer
Adressraum
URL
Information
E-Mail
Analysis
Autorisierung
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Momentenproblem
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Plot <Graphische Darstellung>
Physikalisches System
Teilbarkeit
Algorithmus
Echtzeitsystem
Computerspiel
Mustersprache
Basisvektor
Partikelsystem
E-Mail
Facebook
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Content <Internet>
Ungerichteter Graph
Ein-Ausgabe
Biprodukt
Computeranimation
Gruppenoperation
Netzwerktopologie
Graph
Algorithmus
Gruppentheorie
Rechter Winkel
Dreiecksfreier Graph
Faktor <Algebra>
Information
Bildgebendes Verfahren
Modallogik
Besprechung/Interview
Schlussregel
Computerunterstütztes Verfahren
Polstelle
Leck
Subtraktion
Benutzerfreundlichkeit
Selbst organisierendes System
Datenhaltung
Besprechung/Interview
Systemaufruf
Ordnung <Mathematik>
Virtuelle Maschine
Metadaten
Knotenmenge
Subtraktion
Content <Internet>
Software
Datenanalyse
Besprechung/Interview
Metadaten
Virtuelle Maschine
Prozess <Physik>
Besprechung/Interview
Übergang
Netzwerktopologie
Metadaten
Subtraktion
Besprechung/Interview
Ereignishorizont
Analysis

Metadaten

Formale Metadaten

Titel Metadata Investigation
Untertitel Inside Hacking Team
Serientitel re:publica 2016
Teil 47
Anzahl der Teile 188
Autor Joler, Vladan
Lizenz CC-Namensnennung - Weitergabe unter gleichen Bedingungen 3.0 Deutschland:
Sie dürfen das Werk bzw. den Inhalt zu jedem legalen Zweck nutzen, verändern und in unveränderter oder veränderter Form vervielfältigen, verbreiten und öffentlich zugänglich machen, sofern Sie den Namen des Autors/Rechteinhabers in der von ihm festgelegten Weise nennen und das Werk bzw. diesen Inhalt auch in veränderter Form nur unter den Bedingungen dieser Lizenz weitergeben.
DOI 10.5446/20717
Herausgeber re:publica
Erscheinungsjahr 2016
Sprache Englisch

Inhaltliche Metadaten

Fachgebiet Informatik
Abstract This story is about the power of Metadata. In some kind of reverse engineering process we explored the possibility of using NSA data analysis methodology for an independent data investigation of the Hacking Team email metadata. There is an ongoing debate over the significance of metadata. We wanted to question а somewhat heretical argument that bulk metadata contain sensitive information about private life of internet users and confront it with a ruling opinion that such statement is overrated. We have therefore undertaken the following social and scientific experiment using different methodologies. The purpose of this research is to investigate and consequently inform the scientific and popular audience about the real importance of metadata for our privacy.

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