, 00:00:03:22 ,So welcome to the Dark Arts of OSINT.
, 00:00:07:11
, 00:00:12:28 ,This would be Dr. Noah Schiffman, aka Security Freak.
, 00:00:17:22 ,He is the academic of our team, the one who actually finished college.
, 00:00:21:17 ,(laughter) Yes, he is way more intelligent than I am,
, 00:00:26:29 ,a snappy dresser, and an absolutely wonderful guy.
, 00:00:30:03 ,(Ring) Dude, that's so not cool.
, 00:00:37:00 , SKYDOG: Could I have a red shirt kick the shit out of this guy?
, 00:00:37:09
, 00:00:39:00 ,There we go.
, 00:00:41:00 ,Like they didn't tell us that earlier.
, 00:00:42:12
, 00:00:46:08 ,And I'm SkyDog, of course, by the picture there.
, 00:00:46:16
, 00:00:50:03 ,We are part of the Dead Bunny Club.
, 00:00:50:16
, 00:00:55:12 ,It's the pseudophilanthropic arm of everything SkyDog does.
, 00:00:57:04
, 00:00:59:03 ,We got together.
, 00:01:03:01 ,I met you a couple of years ago and we found that we're fast friends
, 00:01:05:16 ,and we have a lot of fun getting together
, 00:01:08:05 ,and getting into trouble.
, 00:01:09:14
, 00:01:12:01 , Sometimes a little more than friends.
, 00:01:16:26 , We weren't going to talk about that.
, 00:01:27:01 ,I took that out of the presenter notes, dude.
, 00:01:28:01 , NOAH: Sorry.
, 00:01:33:00 , This is my 11th year, back in the AP days.
, 00:01:34:01 ,Round of applause.
, 00:01:35:22
, 00:01:37:20 ,Everyone's a n00b.
, 00:01:43:00 , NOAH: I just heard about DEF CON two weeks ago.
, 00:01:43:12
, 00:01:50:19 , SKYDOG: So I get to celebrate, ironically, at my 11th year year.
, 00:01:52:20 ,I've actually been a goon for nine years.
, 00:01:58:14 ,For my 11th year here I get to celebrate three firsts.
, 00:01:58:21
, 00:02:01:05 ,Don't worry, I haven't learned my virginity.
, 00:02:06:07 , Don't worry, it will happen soon.
, 00:02:08:02 , SKYDOG: I understand I have to talk to a girl, though,
, 00:02:10:12 ,and I'm not ready for that.
, 00:02:18:05 ,(laughter) (applause) So the first one it was really wonderful.
, 00:02:20:25 ,My son got to participate in DEF CON kids.
, 00:02:23:03 ,I'm old enough now that I have offspring.
, 00:02:23:12
, 00:02:24:20 , NOAH: Cooper.
, 00:02:24:26
, 00:02:28:14 , SKYDOG: He placed fourth in social engineering and second
, 00:02:30:17 ,in hacker jeopardy.
, 00:02:40:20 ,(applause) SKYDOG: My second would be my first Mohawk ever.
, 00:02:43:07 ,I got to participate in Mohawk Con this year.
, 00:02:44:29 ,Round of applause for those guys.
, 00:02:49:03 ,(applause) SKYDOG: I had to leave Vanderbilt
, 00:02:52:12 ,to make that happen.
, 00:02:54:26
, 00:02:57:07 ,This is an honor.
, 00:02:57:17
, 00:03:01:09 ,I did find out they require you to submit a paper.
, 00:03:02:01
, 00:03:05:20 ,I didn't read the fine print, but here we are.
, 00:03:08:19 ,We're talking about our live demo.
, 00:03:09:07
, 00:03:15:01 , NOAH: There is this live demo thing that maybe kind
, 00:03:18:19 ,of discussed in the CFP.
, 00:03:22:10 ,Well, I don't know how many people here are familiar
, 00:03:26:26 ,with something called Matlab or R or I don't know, other letters
, 00:03:29:02 ,of the alphabet.
, 00:03:29:12
, 00:03:31:16 ,Yes, what's your favorite letter?
, 00:03:31:23
, 00:03:36:21 ,So I didn't have a licensed copy of Matlab and went with Octave
, 00:03:42:22 ,and got into a battle with Octave and they won and I lost.
, 00:03:43:11
, 00:03:46:05 ,We're doing a different kind of live demo that's sort
, 00:03:49:00 ,of audience participation based.
, 00:03:53:16 ,It will be really fun and everyone will get to meet people sitting
, 00:03:55:13 ,next to you.
, 00:03:58:04 ,It will be a fun icebreaker opportunity no, it's not.
, 00:04:00:28 ,But it's going to be a demo that we can all participate
, 00:04:03:08 ,in and make a point.
, 00:04:04:15
, 00:04:06:02 ,I hate Octave.
, 00:04:07:11 ,I hate it.
, 00:04:08:11 ,Ready?
, 00:04:14:08 , SKYDOG: Get loose.
, 00:04:15:10 ,Here we go.
, 00:04:19:04 ,So our talk today is about the Dark Arts of OSINT.
, 00:04:22:18 ,So the path we'll take, we'll talk about what is OSINT?
, 00:04:22:27
, 00:04:27:12 ,We're going to move on to Evan, if you call me again, I'll fucking kill you.
, 00:04:27:23
, 00:04:28:25 ,I swear.
, 00:04:29:17
, 00:04:32:25 ,Fucking kill you.
, 00:04:33:27 ,I digress.
, 00:04:35:05
, 00:04:37:19 ,So we'll speak about what is OSINT.
, 00:04:38:01
, 00:04:41:10 ,We'll talk about some acquisition tools and techniques.
, 00:04:41:17
, 00:04:44:17 ,I am then going to sit down and the guy with the math background
, 00:04:47:14 ,is going to speak with anonymizing data.
, 00:04:49:04
, 00:04:51:06 ,You don't remember?
, 00:04:52:29 ,Uh huh.
, 00:04:55:08 ,I'm going to leave the stage and Noah is going to speak
, 00:04:58:13 ,about anonymizing and de anonymizing data.
, 00:04:58:29
, 00:05:00:20 ,Open source intelligence.
, 00:05:05:22
, 00:05:08:10 ,Thank you for putting the pause in there.
, 00:05:10:23 ,Did you get the transitions in there?
, 00:05:12:28 , NOAH: I did some.
, 00:05:13:28 ,I forgot.
, 00:05:16:14 , SKYDOG: The cool one that wipes?
, 00:05:17:18 , NOAH: It dissolves.
, 00:05:19:12 , SKYDOG: You pay for the dissolve.
, 00:05:21:08 ,So what is open source intelligence?
, 00:05:24:23 ,Essentially open source intelligence is anything out there that you can reach
, 00:05:27:21 ,without having to be a Leo or something similar or belong
, 00:05:31:29 ,to a large organization that requires paperwork to get to it.
, 00:05:35:16 ,Anything you can get to online or readily available.
, 00:05:35:21
, 00:05:37:17 ,Why do you care?
, 00:05:39:23 ,You had a picture taken by some jackass with a camera,
, 00:05:44:04 ,not one of our photographers, but someone with a phone or whatever.
, 00:05:44:13
, 00:05:49:07 ,Guess what, you're now hooked up with open source.
, 00:05:49:20
, 00:05:51:07 ,The information is out there.
, 00:05:52:13 ,You appear in a picture.
, 00:05:54:19 ,Now it's something I can catalog and index.
, 00:05:55:22 ,So congratulations.
, 00:05:56:23 , NOAH: Prism.
, 00:05:57:02
, 00:06:00:23 , SKYDOG: Weren't going to talk about NOAH: Sorry.
, 00:06:04:20 , SKYDOG: And so how can it be optimized?
, 00:06:07:01
, 00:06:10:06 ,We're looking at big data sets and crunching the numbers
, 00:06:13:14 ,and actually extracting some information some interesting
, 00:06:17:22 ,information out of what's available, readily available.
, 00:06:24:01 ,So OSINT comprises many things.
, 00:06:27:14 ,One of them will be text, whether it is e mails that you sent back
, 00:06:31:21 ,in '73 when you were talking about something bizarre?
, 00:06:34:05 ,Did you send anything back?
, 00:06:35:14 ,Never mind.
, 00:06:39:17 ,I've gone back and found things I've done on forums way, way back
, 00:06:43:01 ,in the day using a different name that I was able
, 00:06:47:13 ,to actually find online, things that probably would have shown
, 00:06:50:26 ,me how ignorant I was at the time.
, 00:06:54:13 ,But anyway, you have text that's out there that could be searched for.
, 00:06:56:02 ,You also have imagery.
, 00:06:57:07 ,We have Facebook.
, 00:06:59:09 ,We have appearing at DEF CON, if you don't realize it or not,
, 00:07:03:26 ,you probably had a picture taken of you at some point in time, video.
, 00:07:06:03
, 00:07:11:23 ,I think last night Evan played the VR system and the robot, which
, 00:07:16:01 ,is an absolute hoot, which will appear on YouTube
, 00:07:19:20 ,with some captioning later on.
, 00:07:21:03
, 00:07:22:28 ,The Black Hat robot.
, 00:07:24:01
, 00:07:26:02 ,We have audio.
, 00:07:28:01 ,The video we have here of this presentation
, 00:07:31:03 ,is currently available on DVD later.
, 00:07:31:14
, 00:07:36:09 ,They also put the audio up of that so if you're not into driving and looking
, 00:07:40:03 ,at your iPhone, you can listen to the audio.
, 00:07:42:06 ,And then you have geospatial, which would be
, 00:07:45:19 ,the images you take from a device that's GPS enabled
, 00:07:49:29 ,and records your longitude and latitude and fun things like that,
, 00:07:53:06 ,other information that doesn't always get removed
, 00:07:56:15 ,from imagery when it's put online.
, 00:07:58:06
, 00:08:01:05 ,There is a certain signal to noise ratio.
, 00:08:04:10 ,If you've been online, if you're looking for data,
, 00:08:08:23 ,there may be some really bizarre things.
, 00:08:12:06 ,Noah never lived in Henderson, Nevada, but for some reason my name
, 00:08:15:12 ,and phone number are associated with them.
, 00:08:15:24
, 00:08:19:06 ,There are certain information out there that doesn't really fall
, 00:08:21:18 ,into place correctly.
, 00:08:24:05 ,You have to go through and decrease the noise
, 00:08:26:23 ,to get the true signal.
, 00:08:27:01
, 00:08:29:24 ,So out of that, once you clean up enough data, you're able
, 00:08:33:00 ,to go through and put enough things together, layer them together,
, 00:08:36:13 ,find where the high points and the graph appear.
, 00:08:36:20
, 00:08:38:16 ,You will find actual data.
, 00:08:40:22 ,Anyone in the law enforcement community,
, 00:08:45:08 ,which I am not, anyone who is in that community realizes that when
, 00:08:49:17 ,enough data is collected, it becomes actionable.
, 00:08:51:08 ,Then it becomes intelligence, something that can be used
, 00:08:53:10 ,to actually do something.
, 00:08:53:20
, 00:09:01:14 ,(Prism) Sorry, I got a little cough there.
, 00:09:04:13
, 00:09:05:22 ,Furball.
, 00:09:08:23 , NOAH: No, I don't want to drink any more.
, 00:09:09:07
, 00:09:11:21 , SKYDOG: Wait until you get on stage.
, 00:09:13:20
, 00:09:17:11 ,Media had newspapers clippings from other parts
, 00:09:21:20 ,of the United States write a report on it.
, 00:09:24:09 ,We moved into the radio age.
, 00:09:27:15 ,Things were transcribed and cataloged and indexed.
, 00:09:27:21
, 00:09:31:08 ,The search time on information like that was a little long, if you want
, 00:09:34:11 ,to claim about more Oracle or mySQL.
, 00:09:38:07
, 00:09:42:17 ,It got compressed down to videotape and things
, 00:09:45:02 ,of that nature.
, 00:09:47:21 ,Like I said, I recently worked for Vanderbilt.
, 00:09:50:25 ,They have the largest compendium of news broadcasts.
, 00:09:52:27 ,They go back farther than anyone else.
, 00:09:53:07
, 00:09:56:24 ,That information can also be searched by metadata.
, 00:09:57:05
, 00:10:00:16 ,Of course, we're down to the Internet age where every
, 00:10:04:02 ,jackass can get out there and dance and then put online
, 00:10:07:24 ,their robot at a large security conference.
, 00:10:09:28 ,That's coming back to haunt you, ass hat.
, 00:10:11:05
, 00:10:16:00 ,So the evolution is new sources, of course, with radio and print.
, 00:10:18:24 ,Then we move to government repositories.
, 00:10:21:17 ,For some reason they decided it would be a good idea
, 00:10:25:04 ,to collect information and store it.
, 00:10:26:07 ,Who knew?
, 00:10:26:18
, 00:10:29:25 ,Then you went to academic publications where
, 00:10:33:14 ,they sorted data and put it together.
, 00:10:35:18 ,Theoretically they anonymized it.
, 00:10:35:23
, 00:10:38:00 ,Now we've moved into the electronic databases where we
, 00:10:40:10 ,know everything about you.
, 00:10:41:05
, 00:10:43:03 ,Those are sexy.
, 00:10:43:11
, 00:10:45:21 ,Those will get you laid, definitely.
, 00:10:46:03
, 00:10:49:22 ,So the current forms and uses of OSINT are definitely tool sets,
, 00:10:53:26 ,websites you can go to, and of course databases you can get your
, 00:10:57:24 ,hands onto, depending on what your flavor is.
, 00:10:59:24
, 00:11:02:01 ,Whoever has used Maltego?
, 00:11:03:20 ,Show of hands.
, 00:11:04:20 ,Cool.
, 00:11:05:20 ,Okay.
, 00:11:10:09 ,So Maltego is basically used you put a click.
, 00:11:11:09 ,Yeah.
, 00:11:13:06 ,Next time I'll let you do this, Bart.
, 00:11:18:14 ,Maltego is used to dig down on an organization.
, 00:11:20:15 ,You can look at whose records and DNS and IP's and e mails
, 00:11:22:25 ,and things of that nature.
, 00:11:23:20
, 00:11:26:27 ,I'll have someone else come up here and stomp your ass, too.
, 00:11:27:23
, 00:11:31:21 ,Maltego is really good for drilling down on a company by looking
, 00:11:36:24 ,at e mail addresses and things to compile a large amount of data.
, 00:11:37:15
, 00:11:39:18 ,Who has used FOCA?
, 00:11:40:20
, 00:11:44:04 ,If you haven't played with FOCA FOCA is a lot
, 00:11:47:09 ,of fun basically it looks at the metadata
, 00:11:51:05 ,in Microsoft Office documents, PDF's.
, 00:11:52:23 ,It will do Open Office.
, 00:11:55:29 ,It looks at the metadata in pictures.
, 00:11:57:14 ,You can begin to compile information just
, 00:12:00:18 ,in the hidden information in all the documents you can get
, 00:12:02:04 ,ahold of.
, 00:12:02:17
, 00:12:05:11 ,Randy from accounting puts out some sort of a document and
, 00:12:08:21 ,inside that it contains information about where it's stored
, 00:12:11:10 ,on the local network, and it actually makes it
, 00:12:14:22 ,to the outside world and gives me some information about how
, 00:12:17:13 ,the interior network is built.
, 00:12:20:18 ,So that one's a really nice fun one to play with.
, 00:12:21:16
, 00:12:22:28 ,SearchDiggity.
, 00:12:23:05
, 00:12:24:15 ,Anyone use that one?
, 00:12:27:02 , NOAH: Not in my backyard.
, 00:12:30:15 , SKYDOG: Do what?
, 00:12:34:29 ,Apparently SearchDiggity isn't used as much as everyone would like.
, 00:12:39:02 ,It basically is another form of being able to sift through data.
, 00:12:44:11 ,It takes information from Bing and Google and compiles it
, 00:12:49:03 ,into a nice interface to get to it.
, 00:12:49:09
, 00:12:52:09 ,A lot of nice pieces of software.
, 00:12:52:18
, 00:12:55:00 ,Anyone heard of Recorded Future?
, 00:12:55:23
, 00:12:57:09 ,This is one of those that makes you kind
, 00:12:59:07 ,of cringe a little bit.
, 00:13:02:03 ,It's a temporal analysis engine.
, 00:13:02:11
, 00:13:06:09 ,It forecasts and does analysis to predict future events based
, 00:13:10:05 ,on information from social networks and patterns that
, 00:13:12:07 ,they can find.
, 00:13:14:15 ,They're able to go in and put some information
, 00:13:17:10 ,in and actually determine what could possibly happen based
, 00:13:20:10 ,on information that's flowing right now.
, 00:13:22:15
, 00:13:25:02 ,Of course there's Facebook.
, 00:13:29:01 ,Who has put their music preferences on who uses Facebook?
, 00:13:31:01 ,It's all right, we're among friends.
, 00:13:32:09 ,You can raise your hands.
, 00:13:32:20
, 00:13:34:07 ,Big mistake.
, 00:13:36:04 ,Could we get a picture of that?
, 00:13:37:08
, 00:13:41:13 ,So if you've put onto Facebook, hey, I like REO Speedwagon.
, 00:13:44:16 ,For all the young guys in the crowd, that's a rocking band.
, 00:13:46:23
, 00:13:49:26 ,Well, I can go back in with graph search now and say, hey,
, 00:13:52:23 ,I want to know anyone who lives in Tennessee who likes
, 00:13:54:18 ,REO Speedwagon.
, 00:13:55:00
, 00:13:59:01 ,And then I can mine some data out, and I guess give you a jingle and say,
, 00:14:02:01 ,hey, why don't you sit around and listen to records,
, 00:14:05:03 ,at which point you would probably run.
, 00:14:06:22
, 00:14:10:02 ,There are things are actually being put out there now for you to be able
, 00:14:13:08 ,to look at the data and try to grind through it.
, 00:14:16:24 ,There are other websites, social mentions, Spokio,
, 00:14:22:11 ,I have my own personal preferences on what to use.
, 00:14:25:09 ,Johnny Long isn't here, but who has ever seen
, 00:14:28:04 ,the Google hacking database?
, 00:14:28:16
, 00:14:29:21 ,Okay.
, 00:14:33:20 ,So a bunch of things that people have put together.
, 00:14:34:24 ,If you're looking for certain types of information,
, 00:14:38:00 ,they've put query structures together for you to use.
, 00:14:42:00
, 00:14:46:12 ,This is what it's like to hang out with Noah and at any point in time.
, 00:14:49:08
, 00:14:53:08 ,Basically you have three different types of public data.
, 00:14:55:02
, 00:14:58:25 ,You have cooperatively provided data, which would be this is my name
, 00:15:01:23 ,and this is what I like, which is social networking,
, 00:15:04:13 ,it's what I put on Facebook.
, 00:15:07:11 ,I like REO Speedwagon and Smurfs.
, 00:15:14:07
, 00:15:17:01 ,It's things that you put out there that your personal
, 00:15:20:08 ,preferences or posts that you can make that can be mined to look
, 00:15:23:08 ,at but you've willingly given it up.
, 00:15:25:05 ,Did I say that right?
, 00:15:26:05 , NOAH: Yes.
, 00:15:27:05 , SKYDOG: Okay.
, 00:15:28:05 ,Just checking.
, 00:15:31:04 ,Things that are confidentially provided, a session ID.
, 00:15:34:12 ,I had to log in to give that information.
, 00:15:37:05 ,I filled out a questionnaire or survey.
, 00:15:41:24 ,I said, "Yes, I'm more than happy to allow you to look at this information."
, 00:15:44:07 ,I put something in there enough that it's very
, 00:15:48:09 ,identifiable, be it my address, my phone number, my credit card,
, 00:15:50:21 ,things of that nature.
, 00:15:54:20 ,So you have to actually it's a site with a privacy policy where you say
, 00:15:56:18 ,I agree to it.
, 00:15:56:26
, 00:15:59:22 ,So you've given that information up and you've agreed
, 00:16:02:23 ,to their legal statement there.
, 00:16:03:02
, 00:16:05:09 ,Then you have the unknowingly provided
, 00:16:08:22 ,or where did they get this from?
, 00:16:12:10 ,It's the DMV records.
, 00:16:14:01 ,It's other information.
, 00:16:15:25 ,Maybe it's your medical records or how the fuck did
, 00:16:18:06 ,they get my APGAR scores?
, 00:16:19:10
, 00:16:23:06 ,He was slow at birth and it never got better.
, 00:16:24:19
, 00:16:28:21 ,Government and academia whoever participated in something
, 00:16:31:29 ,in college where you paid $20 for an ass probe
, 00:16:34:24 ,or something for research.
, 00:16:35:20
, 00:16:38:12 ,So they take that data and they put it into a database and
, 00:16:40:12 ,they put it online.
, 00:16:43:02 ,Theoretically your name's not associated with it.
, 00:16:47:06
, 00:16:50:02 ,So who publishes these data sets?
, 00:16:51:22 ,A lot of times it's government.
, 00:16:53:23 ,There's academia.
, 00:16:58:13 ,There is a commercial market for data that's been pieced together.
, 00:16:59:08
, 00:17:04:04 ,For a certain fee, you can go in and cruise through that data.
, 00:17:04:13
, 00:17:06:27 ,The more you pay, the more granular your data becomes
, 00:17:09:17 ,and the more revealing it is.
, 00:17:10:22
, 00:17:13:20 ,Why are these data sets published?
, 00:17:18:05
, 00:17:20:24 ,For statistical analysis, we want to go back and look
, 00:17:24:01 ,at the information and do some predictions.
, 00:17:26:28 ,Looking for trends and patterns that are out there.
, 00:17:27:07
, 00:17:29:20 ,Retrospective outcomes.
, 00:17:32:11
, 00:17:37:07 ,We struggle trying to find the proper example of this.
, 00:17:37:19
, 00:17:42:09 ,We decided on which is better, Viagra or Cialis.
, 00:17:42:18
, 00:17:45:21 ,We look at the information and see the satisfaction I guess that's not
, 00:17:47:20 ,the right terminology.
, 00:17:49:10 , NOAH: I said Viagra.
, 00:17:50:23
, 00:17:55:06 , SKYDOG: A buddy of mine, I swear, said Cialis.
, 00:17:57:17
, 00:17:59:29 , NOAH: It was a friend of mine, too.
, 00:18:01:14 , SKYDOG: No, no, it wasn't.
, 00:18:02:14 , NOAH: Evan?
, 00:18:03:27 , SKYDOG: Where did Evan go?
, 00:18:04:27 ,He's hiding.
, 00:18:06:01 ,That's good.
, 00:18:10:04 ,So of course this information is used for decision making
, 00:18:14:20 ,for future things, maybe it is product design or coming
, 00:18:18:24 ,up with something new, whether it's actually going
, 00:18:23:06 ,to be popular in any way, shape, or form.
, 00:18:23:18
, 00:18:27:27 ,A lot of the things that are using here, the tools on the websites,
, 00:18:30:04 ,I don't do the math.
, 00:18:30:10
, 00:18:32:25 ,That's this gentleman's side of things.
, 00:18:33:07
, 00:18:36:00 ,Occasionally I get asked to find things.
, 00:18:39:26 ,Who in the crowd, who finished high school?
, 00:18:41:17 ,Show of hands.
, 00:18:42:17 ,It's okay.
, 00:18:44:03 ,All right.
, 00:18:45:05 ,Who went to college?
, 00:18:45:29
, 00:18:48:02 ,Now, who finished college?
, 00:18:48:23
, 00:18:50:00 ,Okay.
, 00:18:50:08
, 00:18:52:01 ,This is your crowd.
, 00:19:00:17 ,So anyway (laughter) Do you want to do that?
, 00:19:01:17 ,No.
, 00:19:05:17 ,So I did not finish college.
, 00:19:10:01 ,I had a hell of a lot of fun while I was there, per my GPA,
, 00:19:18:05 ,but what I did not learn while I was at college is what you can and can't do.
, 00:19:18:18
, 00:19:22:22 ,It was not taught out of me, oh, you can't do it that way.
, 00:19:24:14 ,So I never heard that before.
, 00:19:27:05 ,And I don't pay attention to it, so it makes it a lot easier for me
, 00:19:30:15 ,to do some things like drill data on somebody.
, 00:19:33:16 ,Occasionally I'll get a phone call and I'll get a couple pieces of criteria,
, 00:19:35:26 ,and they say "find someone."
, 00:19:36:02
, 00:19:39:19 ,And I've become very a dept at doing that using the open source.
, 00:19:43:06
, 00:19:45:13 ,Is anyone staying at the Bellagio?
, 00:19:47:26
, 00:19:52:15 ,Cabana by the refrigerated pool.
, 00:19:52:29
, 00:19:55:12 ,If at any point in your lifetime you can make that
, 00:19:57:21 ,happen, definitely do it.
, 00:19:59:01 ,I'm in the sun.
, 00:20:01:07 ,I've got the Mac Book Air with me.
, 00:20:03:19 ,I'm trying to get on the shitty wireless there that doesn't
, 00:20:07:14 ,work, and there's a gentleman to my immediate right.
, 00:20:11:21 ,He notices I have a computer, which for all of us is the sticking point to,
, 00:20:15:18 ,yeah, dude, my computer at home doesn't work.
, 00:20:16:22
, 00:20:19:07 ,Whoever has answered that question?
, 00:20:19:15
, 00:20:24:19 ,I'm in a swimsuit by the pool and a guy starts talking to me.
, 00:20:25:19 ,Okay.
, 00:20:26:19 ,I'll bite.
, 00:20:27:19 ,No problem.
, 00:20:31:09 ,So we start discussing China, politics, the economy, fun things like that
, 00:20:34:00 ,to really make you happy.
, 00:20:34:05
, 00:20:36:08 ,We have a few drinks.
, 00:20:38:29 ,And he says, "You know, so, you're in Vegas.
, 00:20:40:15 ,Are you here for business or pleasure?"
, 00:20:43:20 ,And I said, "Currently for pleasure."
, 00:20:46:08 ,I would think that's the case if I'm by the pool.
, 00:20:50:03 ,And he says, "So you're here for pleasure.
, 00:20:51:03 ,That's good."
, 00:20:53:09 ,And I said, "Well, actually, in two or three weeks I'm coming back
, 00:20:58:09 ,out to the largest hacker conference in the United States called DEF CON."
, 00:20:58:19
, 00:21:01:08 ,And you could hear his asshole pucker in the seat.
, 00:21:05:01
, 00:21:07:17 ,(laughter) That's one of those things where who
, 00:21:11:11 ,in the crowd hasn't had to explain what that means?
, 00:21:11:18
, 00:21:13:22 ,Put your hand down, ass hat.
, 00:21:14:12
, 00:21:17:26 ,So I began to explain what DEF CON is.
, 00:21:22:03 ,Since we didn't have the documentary, it was very interesting
, 00:21:24:26 ,to explain it to him.
, 00:21:25:01
, 00:21:27:01 , NOAH: It's the hearing impaired con.
, 00:21:30:15 , SKYDOG: I got to explain to him what we do and why we get
, 00:21:33:06 ,together for all that.
, 00:21:33:19
, 00:21:36:12 ,And then his jackass friend shows up.
, 00:21:41:12 ,He had come to Vegas to go to the Pawn Stars place downtown.
, 00:21:43:17 ,And he said, "Dude, I got to meet Hoss.
, 00:21:48:13 ,Okay, let's go get a steak."
, 00:21:52:03
, 00:21:57:19 ,We're going to head off to get a steak at so and so place, nice meeting you.
, 00:21:57:25
, 00:21:58:26 ,Later.
, 00:22:00:19
, 00:22:03:21 ,I said your name is Brian, and your family owns
, 00:22:07:27 ,a civil construction firm in Seattle, Washington.
, 00:22:08:10
, 00:22:11:03 ,And the guy says, "Yeah."
, 00:22:14:03 ,And I said, "I'll send you an e mail to your work e mail
, 00:22:16:16 ,within the next 48 hours."
, 00:22:17:01
, 00:22:19:04 ,Again, you could hear his asshole pucker.
, 00:22:20:16
, 00:22:24:06 ,And I said, "Don't worry, I'm gonna show you.
, 00:22:26:11 ,I have two bits of information on you.
, 00:22:28:01 ,I don't have your last name.
, 00:22:30:26 ,I don't have much more than that, but I'm gonna send you an e mail
, 00:22:33:17 ,and show you what's possible."
, 00:22:34:00
, 00:22:38:04 ,So we went out and had a nice dinner, went out to the pool the next day.
, 00:22:41:07 ,At some point I thought, I got to go find Brian.
, 00:22:44:03 ,So I sit down on the bed and fire up the laptop.
, 00:22:47:16 ,In 45 minutes, I owned this guy.
, 00:22:47:25
, 00:22:49:16 ,I have where he lives.
, 00:22:51:25 ,Pictures of his house, what he paid for.
, 00:22:53:23 ,Pictures of all of his relatives.
, 00:22:54:00
, 00:22:56:23 ,I then took it upon myself to scan the exterior of his network
, 00:23:00:10 ,and tell his system administrator you probably should change this;
, 00:23:03:02 ,it's not good to have this open.
, 00:23:03:14
, 00:23:06:29 ,Brian never responded to the e mail, oddly enough.
, 00:23:08:20
, 00:23:12:01 ,I didn't send him an invoice.
, 00:23:14:04 ,I did it gratis.
, 00:23:17:01 ,But that's a good example of I had two bits of information
, 00:23:18:22 ,on the guy.
, 00:23:20:24 ,Fortunately, one of them was unique enough,
, 00:23:23:13 ,it allowed me to find him.
, 00:23:25:04 ,I was able to correlate civil construction,
, 00:23:28:09 ,oddly enough, against the YouTube video which I was
, 00:23:33:18 ,able to pick this guy out in, and from there just went to town on him.
, 00:23:36:00 ,So I guess if you get an e mail from a guy that you met
, 00:23:39:09 ,by the pool who is a hacker and he says he has a picture
, 00:23:44:00 ,of the house from the driveway, it might be a little unnerving.
, 00:23:46:29 , NOAH: Was that legal?
, 00:23:51:13 , SKYDOG: I don't give a shit.
, 00:23:52:14
, 00:23:54:12 ,(laughter) I don't have to have a court order,
, 00:23:56:24 ,and apparently no one else does.
, 00:23:58:15
, 00:24:01:23 ,(laughter) Anyhow, the open source side of it can be
, 00:24:03:22 ,a lot of fun.
, 00:24:06:11 ,One of the things that Noah is going to discuss is finding outliers
, 00:24:07:29 ,in the data.
, 00:24:08:05
, 00:24:10:21 ,Brian had enough for me to be able to find.
, 00:24:13:14 ,Had he said my name is John, it would probably be
, 00:24:16:11 ,a little bit more difficult.
, 00:24:24:07 ,If he says, yeah, I work at Starbucks, not as much of an outlier,
, 00:24:32:00 ,but it took me about 45 minutes to track him down.
, 00:24:32:06
, 00:24:35:15 ,If you ever get bored and you're by the pool at Bellagio, just wait
, 00:24:37:18 ,for someone to come by.
, 00:24:38:20 ,It's a lot of fun.
, 00:24:43:03 , NOAH: You like talking to guys at pools, don't you?
, 00:24:45:29
, 00:24:49:21 ,(laughter) SKYDOG: Have you ever been given a wedgy on stage?
, 00:24:51:24
, 00:24:54:08 ,(laughter) SKYDOG: You take the microphone.
, 00:24:56:03
, 00:24:58:15 , NOAH: Wow.
, 00:25:00:11
, 00:25:04:18 ,(laughter) NOAH: Sky claimed that I'm gonna talk about a lot
, 00:25:08:01 ,of things that I don't know where he got that from,
, 00:25:12:01 ,but SKYDOG: You were really, really drunk.
, 00:25:19:04 , NOAH: I know a little bit of math, some basic addition, subtraction stuff.
, 00:25:19:19
, 00:25:22:19 ,I'm not going to talk about anything hard in advance,
, 00:25:25:14 ,because that's for smart people.
, 00:25:27:11 ,A lot of these slides hello?
, 00:25:28:11 ,Hello?
, 00:25:29:11 ,Where is the echo.
, 00:25:32:21 ,I don't like that echo.
, 00:25:36:00 , SKYDOG: I picked them out of other people's sets.
, 00:25:37:00 ,Have fun.
, 00:25:39:18 , NOAH: Dammit.
, 00:25:40:19 ,Okay.
, 00:25:41:03
, 00:25:46:08 ,These slides are semi new to me, but I think I did make them.
, 00:25:47:18 ,So let's go through them.
, 00:25:48:24 ,Data science.
, 00:25:52:21
, 00:25:55:20 ,The science of data.
, 00:25:57:22 ,Science has been around for a long time.
, 00:25:58:03
, 00:25:59:29 ,Data has been around for a long time.
, 00:26:04:26 ,You put them together and it's (laughter) it's emerged mostly
, 00:26:10:06 ,over the past decade to be really the real data science,
, 00:26:13:11 ,information scientists.
, 00:26:16:03 ,It's been the past decade kind of thing.
, 00:26:16:13
, 00:26:20:13 ,It sort of came out of the whole business analytics
, 00:26:23:12 ,competitive intelligence.
, 00:26:24:01
, 00:26:26:06 ,Like everything else, driven by big business,
, 00:26:29:29 ,because they're just looking out for our best interest.
, 00:26:31:06
, 00:26:38:21 ,So all of a sudden people who were data mining and mathematical analysis
, 00:26:44:25 ,are very valuable to big businesses and other entities that
, 00:26:49:06 ,like to analyze large data sets.
, 00:26:49:17
, 00:26:52:06 ,Are there other entities that collect lots of data?
, 00:26:53:29 , SKYDOG: None that I've heard of.
, 00:26:56:03 , NOAH: I haven't heard of any either.
, 00:26:58:20 ,But I'm sure there are organizations out there that are collecting lots
, 00:27:01:19 ,of data and doing something with this.
, 00:27:02:22
, 00:27:06:28 , SKYDOG: Purely for benevolent reasons.
, 00:27:08:05 , NOAH: Yeah, exactly.
, 00:27:08:10
, 00:27:11:07 ,But it's mostly to enhance our shopping
, 00:27:13:17 ,experience; right?
, 00:27:17:15 ,Like other people who bought this also bought this.
, 00:27:18:24
, 00:27:24:14 ,Statistics, just you're given data, try to come up with a model, probability,
, 00:27:28:25 ,given a model, let's try to predict the data.
, 00:27:29:03
, 00:27:30:07 ,Simple concept.
, 00:27:31:08 ,Okay.
, 00:27:33:12
, 00:27:38:09 ,Here is a little graphic demonstrating what I just said, and it's useless.
, 00:27:38:19
, 00:27:40:05 ,Historic data model, ignore.
, 00:27:40:29
, 00:27:43:23 ,Data sources.
, 00:27:44:24 ,Okay.
, 00:27:45:02
, 00:27:51:06 ,These are some random examples of readily available public data sets.
, 00:27:51:25
, 00:27:55:15 ,We've actually gone from, like, having database information
, 00:28:00:13 ,to databases that are cataloging the databases of information.
, 00:28:00:20
, 00:28:03:02 ,It's increasing exponentially.
, 00:28:04:12
, 00:28:09:01 ,My favorite was Free Base I came across when I was searching
, 00:28:13:27 ,for something else, but apparently it's a database.
, 00:28:15:12
, 00:28:17:18 ,(laughter) I also like Info Chimps.
, 00:28:22:22
, 00:28:24:18 ,Big data.
, 00:28:24:26
, 00:28:27:12 ,Not just any data, but big data.
, 00:28:27:23
, 00:28:31:10 ,Buzz word, who thinks it's a buzz word?
, 00:28:35:21
, 00:28:40:12 ,Some other people think it's a legitimate, real thing?
, 00:28:42:04 ,That's cool.
, 00:28:43:06 ,I don't judge.
, 00:28:43:29
, 00:28:47:21 ,Well, I don't know.
, 00:28:48:07
, 00:28:51:29 ,It's hard to define what that really means,
, 00:28:57:05 ,big data, like is it big data is it in the Cloud?
, 00:28:59:15 , SKYDOG: It's a large type face.
, 00:29:03:04 , NOAH: What's the cutoff for being big?
, 00:29:03:17
, 00:29:05:05 ,8 inches?
, 00:29:06:05 ,10 inches?
, 00:29:08:19 ,What does it become really big?
, 00:29:08:24
, 00:29:10:20 ,Sky, how big is your data.
, 00:29:12:01 , SKYDOG: My data is huge.
, 00:29:16:06 , NOAH: I work with a very small data set,
, 00:29:19:28 ,and I'm okay with that.
, 00:29:21:01 ,(laughter) SKYDOG: And at this point this
, 00:29:25:19 ,is yet another presentation we cannot put in our portfolio for public speaking.
, 00:29:27:29 , NOAH: Oh, boy, that's true.
, 00:29:28:07
, 00:29:32:22 ,So technically, at least what I found is that it sort of defined
, 00:29:38:21 ,as big data incredibly large amounts of data that are being rapidly generated
, 00:29:42:00 ,and have lots of variability.
, 00:29:42:28
, 00:29:44:07 ,Okay.
, 00:29:44:21
, 00:29:46:00 ,Sure.
, 00:29:47:05
, 00:29:50:17 ,But it's still big at that time.
, 00:29:50:22
, 00:29:55:07 ,But the interesting thing about it, from our perspective, is that
, 00:30:00:05 ,the creation of big data has also sort of brought forth the development
, 00:30:05:15 ,of tools to work with big data to analyze these big data sets.
, 00:30:08:22
, 00:30:12:21 ,All these new mathematical advanced platforms for performing all kinds
, 00:30:16:20 ,of functions on big data, which is of interest to us.
, 00:30:19:02 ,We're going to look at that in a few minutes.
, 00:30:19:23
, 00:30:21:00 ,Okay.
, 00:30:22:00
, 00:30:23:09 ,Terminology.
, 00:30:24:06
, 00:30:28:18 ,That means sort of that of defining words felt.
, 00:30:32:29 , SKYDOG: We Googled it back stage.
, 00:30:34:12 , NOAH: A lot of Googling.
, 00:30:34:29
, 00:30:39:05 ,Depending on what publication you read
, 00:30:45:21 ,or what book, anonymization, mean the same thing.
, 00:30:45:29
, 00:30:52:02 ,De anonymization kind of mean the same thing.
, 00:30:54:07
, 00:30:59:08 ,Some groups will switch for the purposes of our talk, yeah,
, 00:31:02:25 ,they're synonymous, but sort of antonyms,
, 00:31:06:02 ,opposite meaning antonyms.
, 00:31:10:15
, 00:31:14:09 ,You reverse one of these processes, you revert to the other.
, 00:31:15:13
, 00:31:20:23 ,Anyone with a fifth grade background should be able to do it.
, 00:31:21:15
, 00:31:23:21 ,This is simple stuff.
, 00:31:24:06
, 00:31:30:13 ,Data, when it's initially collected, a lot of times it contains personally
, 00:31:34:29 ,identifiable information, like Social Security number
, 00:31:39:10 ,or address or something else, your name.
, 00:31:41:13 ,That would be personally identifiable.
, 00:31:42:01
, 00:31:45:20 ,So there needs to be some kind of process that takes this data
, 00:31:48:16 ,and makes it sort of anonymous.
, 00:31:48:22
, 00:31:50:06 ,I love you, too.
, 00:31:52:10 ,Oh, what was that?
, 00:31:53:01
, 00:31:54:10 ,Ten.
, 00:31:57:01 , SKYDOG: That was ten.
, 00:31:58:01 , NOAH: Holy crap.
, 00:32:00:04 ,Oh, dude, you took up all the damn time.
, 00:32:00:10
, 00:32:01:11 ,Damn.
, 00:32:02:21 ,Wow.
, 00:32:03:21 ,Okay.
, 00:32:06:10 ,So we need to find a way to make this personal
, 00:32:08:22 ,information what?
, 00:32:09:23 ,Okay.
, 00:32:10:04
, 00:32:12:22 ,Make it into anonymous public data.
, 00:32:14:24 ,So there's a couple of different ways it can be done
, 00:32:18:09 ,in general, removing variables all together.
, 00:32:23:08 ,A variable that actually is unique enough to be identifying
, 00:32:29:08 ,by itself, like, you know, I've had eight kids and been in porn,
, 00:32:32:09 ,Octomom, remove those.
, 00:32:35:01
, 00:32:39:11 ,Global re coding, local suppression, where re coding certain variables
, 00:32:43:09 ,or suppression certain values in certain columns that are really
, 00:32:47:04 ,identifiable, a whole bunch of different ways.
, 00:32:48:05 ,Okay.
, 00:32:49:22
, 00:32:51:15 ,Anomyzation metrics.
, 00:32:54:26 ,We have to look at the way we no one's data.
, 00:32:55:08
, 00:32:56:21 ,Is this working?
, 00:33:02:14 ,Is it making the data anonymous or at the same time making it usable,
, 00:33:08:01 ,the whole utility, that's a balance right there.
, 00:33:13:01 ,So two matrix, disclosure risks, like revealing data in the public set,
, 00:33:18:24 ,and then information retention, how utility of that data.
, 00:33:21:01 ,So we take away all this information.
, 00:33:23:05 ,Oh, it's anonymous, but is it still usable.
, 00:33:25:02 ,That's a balance you have to strike.
, 00:33:25:11
, 00:33:26:15 ,It's a tough problem.
, 00:33:26:26
, 00:33:32:14 ,You want to minimize disclosure risk, maximize information retention.
, 00:33:37:17 ,Easier said than done, but information Intropy, and not
, 00:33:43:26 ,the entropy from thermodynamics, which I sent a long semester trying
, 00:33:46:06 ,to go through.
, 00:33:47:12
, 00:33:50:13 ,Information theory, so the idea of is ten minutes.
, 00:33:51:09
, 00:33:55:00 ,I have like a million slides to go through.
, 00:33:55:21
, 00:33:59:29 ,Basically the amount of information that can be
, 00:34:06:11 ,the number of states that can reveal the total number of possibilities
, 00:34:11:02 ,for a given state, like the I use an eight sided die
, 00:34:16:26 ,in an example that obviously you can roll and you get, like, one
, 00:34:21:23 ,through eight because it's got eight sides.
, 00:34:22:03
, 00:34:26:24 ,Information will be three bits, yeah, so population of the world,
, 00:34:31:17 ,let's just say 8 billion, that's like 33 bits.
, 00:34:34:00 ,Awesome websites, 33 bits.org.
, 00:34:36:12
, 00:34:38:17 ,I'll cruise over.
, 00:34:41:04
, 00:34:43:22 ,Everyone participate in something real quick.
, 00:34:46:02 ,We got to do something.
, 00:34:47:02 ,Get up and dance.
, 00:34:50:06 ,(applause) Do we have time for this?
, 00:34:52:18 , SKYDOG: I think we have all the time we want.
, 00:34:53:00
, 00:34:54:18 , NOAH: You got that pull?
, 00:34:59:14 , SKYDOG: I didn't do that.
, 00:35:00:16 ,Wrong?
, 00:35:03:15 ,Let me get the radio and get a couple of red shirts in there.
, 00:35:04:24 , NOAH: We're on slide 26.
, 00:35:07:20 , SKYDOG: We were gonna look at audience participation and kind
, 00:35:12:13 ,of go through and sort people out based on some criteria.
, 00:35:13:21 ,We can skip it, if you want, or if you want to stand
, 00:35:15:17 ,up and raise your hand.
, 00:35:18:03 ,Do you want to do that?
, 00:35:18:09
, 00:35:19:20 , NOAH: All right.
, 00:35:20:20 ,Cool.
, 00:35:21:20 ,How about this.
, 00:35:22:20 ,First question.
, 00:35:25:12 ,Everyone here who this is their first time attending DEF CON,
, 00:35:27:14 ,please stand up.
, 00:35:29:17 , SKYDOG: Nope, nope, nope, nope, nope.
, 00:35:37:29 , NOAH: Come up with west coast, east coast or age cut off?
, 00:35:39:11 , SKYDOG: Tell you what.
, 00:35:44:00 ,Anyone from the east coast stay standing.
, 00:35:45:11 ,Everyone else set down.
, 00:35:46:15
, 00:35:49:20 ,You guys paid the highest airfares, thank you very much.
, 00:35:56:01 , NOAH: Anyone here from New Jersey up?
, 00:35:57:05
, 00:35:59:05 ,I didn't say what to do.
, 00:36:00:28 ,I said anyone from New Jersey up?
, 00:36:03:08 , SKYDOG: Simon says.
, 00:36:04:21 , NOAH: No, you can sit down.
, 00:36:05:21 ,Okay.
, 00:36:09:20 , SKYDOG: Have we got seven or eight or ten people?
, 00:36:10:14
, 00:36:13:21 , NOAH: What are the states below New Jersey.
, 00:36:17:01 , SKYDOG: No, no, no, I was going to say if you have a hangover.
, 00:36:19:20 ,I guess that's not publicly data available.
, 00:36:26:19
, 00:36:28:28 ,If you're female, raise your hand.
, 00:36:29:16
, 00:36:31:06 ,Shitty data set.
, 00:36:34:29 , NOAH: No, and actually, that would be the unit.
, 00:36:36:00 ,There we go.
, 00:36:37:10
, 00:36:42:26 , SKYDOG: Say 29 years of age or younger.
, 00:36:45:03
, 00:36:49:00 ,All the old fucks in the room, sit down.
, 00:36:50:20
, 00:36:52:05 ,Oh, man.
, 00:36:53:21
, 00:36:59:19 , NOAH: Anyone living below North Carolina or South Carolina
, 00:37:02:09 ,border sit down.
, 00:37:03:05
, 00:37:05:19 , SKYDOG: Did we do New Jersey and up?
, 00:37:08:19 , NOAH: Yeah, we're now North Carolina and jersey.
, 00:37:10:26 ,(laughter) who do we have?
, 00:37:14:14 , SKYDOG: You said New Jersey and up.
, 00:37:15:04
, 00:37:17:04 ,You're in the upper quadrant.
, 00:37:19:20 , NOAH: I don't know what the fuck I'm doing.
, 00:37:21:12 , SKYDOG: We can't do male female.
, 00:37:22:24 ,Who got laid last night?
, 00:37:23:10
, 00:37:26:02 ,That's bad data set, too.
, 00:37:26:08
, 00:37:30:06 ,(laughter) SKYDOG: So how many people are we up to?
, 00:37:31:21 ,Who is remaining standing?
, 00:37:32:21 ,Count them off.
, 00:37:34:16 ,I can't see for the lights.
, 00:37:35:22
, 00:37:38:08 ,How many people are in the room right now?
, 00:37:40:14 ,7 or 8,000.
, 00:37:40:24
, 00:37:46:09 ,Up to that, we're down to three people remain standing.
, 00:37:46:21
, 00:37:48:02 ,How many questions?
, 00:37:50:24
, 00:37:52:07 ,Like five questions.
, 00:37:55:12 , NOAH: Well, it was maybe four or five questions.
, 00:38:04:12 ,The entropy for those questions, west coast/east coast, is one bit.
, 00:38:09:14
, 00:38:14:01 , SKYDOG: First time at DEF CON.
, 00:38:16:24 , NOAH: Two bits.
, 00:38:17:02
, 00:38:23:02 , SKYDOG: Anyone above New Jersey and above?
, 00:38:26:29 , NOAH: Pretty much I think all the questions were two bit.
, 00:38:27:14
, 00:38:29:12 ,So five.
, 00:38:31:10
, 00:38:34:22 ,Basically five bits of entropy and we are able
, 00:38:39:22 ,to narrow down the population to three or four people.
, 00:38:41:18 ,And it's all innocuous information.
, 00:38:44:20 ,The point is that the combination of all this innocuous information can
, 00:38:47:13 ,actually be quite identifiable.
, 00:38:48:05
, 00:38:50:13 , SKYDOG: Thank you for participating.
, 00:38:52:08 ,A round of applause for yourselves.
, 00:38:58:11
, 00:39:01:25 , NOAH: I have 20.
, 00:39:16:02
, 00:39:20:16 ,Outliers, single outliers, easy to pick them up if you have them
, 00:39:25:17 ,in combinations or set or a little bit trickier to detect.
, 00:39:27:08 ,Mathematically possible.
, 00:39:27:15
, 00:39:30:00 ,Graphical example of an outlier.
, 00:39:32:07
, 00:39:37:27 ,Everyone here in the audience was an outlier.
, 00:39:40:08
, 00:39:41:21 ,Data sets.
, 00:39:45:18
, 00:39:47:15 ,You have sets of data.
, 00:39:47:23
, 00:39:53:05 ,You have set A, set B, what's the intersection there?
, 00:39:53:07 ,A.
, 00:39:54:08 ,Look at that A and B.
, 00:39:55:26 ,Amazing.
, 00:39:59:23 ,Now you add C, look what you have.
, 00:40:03:00
, 00:40:03:18 ,A and C, B and C.
, 00:40:05:22 ,What do you have in the middle?
, 00:40:06:04
, 00:40:08:03 ,Holy crap.
, 00:40:11:21 ,Isn't that amazing?
, 00:40:15:27 ,(laughter) SKYDOG: That's the math thing happening.
, 00:40:18:28
, 00:40:21:23 , NOAH: Unique variable overlap.
, 00:40:26:18 ,You know, yeah, if you have outliers for different types of data and
, 00:40:30:29 ,they you know what, just move on mathematical tacts
, 00:40:33:16 ,with three minutes.
, 00:40:34:24 , SKYDOG: Slow down.
, 00:40:34:29
, 00:40:37:12 ,Just do it.
, 00:40:38:19 ,Who gives a shit.
, 00:40:39:03
, 00:40:40:25 ,I got it covered.
, 00:40:41:29 , NOAH: Sweet.
, 00:40:46:15 ,Inferential analysis, and the example of it, remember
, 00:40:51:21 ,the targeted advertising, the teenage woman who was pregnant
, 00:40:56:04 ,and was getting all this targeted advertising based
, 00:40:59:29 ,on her purchasing behavior to her household,
, 00:41:05:07 ,and then her dad was upset that she was getting targeted ads
, 00:41:11:19 ,for Enfamil and diapers and got all pissed off at the manager.
, 00:41:14:02 ,And she was pregnant and that's not a good way to find
, 00:41:19:05 ,out and tell your parents you're pregnant is through targeted ads.
, 00:41:20:24
, 00:41:23:24 ,That's not how I'll tell my parents.
, 00:41:26:25
, 00:41:29:05 ,The whole Netflix, IMDb.
, 00:41:30:17 ,You all remember that?
, 00:41:31:20
, 00:41:38:02 ,The census, you know they don't come to the door.
, 00:41:39:22
, 00:41:42:05 , SKYDOG: I don't answer the door.
, 00:41:43:22 ,Me and my 12 roommates.
, 00:41:44:05
, 00:41:48:21 , NOAH: They still come to the door?
, 00:41:50:29 ,Another reason not to answer the door.
, 00:41:55:19 ,Actually, so a researcher in 1990, Latanya Sweeney, using
, 00:42:02:25 ,the information from the census data, date of birth, zip code, 80 percent
, 00:42:07:22 ,of the information was unique, based on principles
, 00:42:11:02 ,of information entropy.
, 00:42:12:01
, 00:42:15:01 ,Exposed healthcare records of the governor of Massachusetts
, 00:42:17:28 ,at the time, which is kind of funny.
, 00:42:19:25 ,And screw you.
, 00:42:23:22
, 00:42:27:24 ,Zip code, there's 43,000 zip codes, birthdate 365.
, 00:42:30:12
, 00:42:34:09 ,Birth year, about 70 different age ranges
, 00:42:41:05 ,of two different genders, 30 bits of entropy includes all the population
, 00:42:43:06 ,in the U.S.
, 00:42:44:07 ,Simple as that.
, 00:42:45:04
, 00:42:46:11 ,PGP.
, 00:42:47:17 ,Ever heard of PGP?
, 00:42:49:00 ,Personal gender project?
, 00:42:49:29
, 00:42:54:14 ,This is where people voluntarily submit all this genetic information
, 00:42:56:17 ,about themselves.
, 00:43:03:09 ,They want to correlate genotype, phenotype to learn about themselves.
, 00:43:03:21
, 00:43:05:07 ,Oh, dude.
, 00:43:08:11
, 00:43:10:01 ,Look, anyway.
, 00:43:12:28 ,Again, this is a project gone bad.
, 00:43:17:06 ,(laughter) No one saw that.
, 00:43:19:12 ,That didn't happen.
, 00:43:22:04
, 00:43:23:26 ,Record linkage.
, 00:43:26:24 , SKYDOG: This is a cool background.
, 00:43:28:02 , NOAH: Take care of him.
, 00:43:29:16 ,He's stressing me out.
, 00:43:31:00 ,Record linkage.
, 00:43:35:19 ,This is where you have a public data set and a private data set.
, 00:43:39:02 ,Public data set maybe that is metadata that's publicly available
, 00:43:42:17 ,and might have some innocuous but identifying information
, 00:43:44:29 ,in about an individual.
, 00:43:47:26 ,The private data said well, that's got personal information you
, 00:43:50:12 ,don't want people to know.
, 00:43:55:04 ,Through record linkage it's possible to actually correlate the two
, 00:43:58:02 ,and discover sort of these anonymous
, 00:44:02:23 ,or so called anonymous traits about a person by combining
, 00:44:05:08 ,the two data sets.
, 00:44:05:18
, 00:44:07:08 ,And I'll get to them mathematically how
, 00:44:11:14 ,to do that in a second, or not if I get kicked off stage.
, 00:44:12:07
, 00:44:13:15 ,All right.
, 00:44:15:01 ,Flying through these slides.
, 00:44:16:01 ,Vectors.
, 00:44:18:05 ,This is where you get into the math.
, 00:44:18:12
, 00:44:23:13 ,So either go to sleep or those anyone math torn?
, 00:44:23:26
, 00:44:24:27 ,Okay.
, 00:44:25:11
, 00:44:29:14 ,Your data points now become a vector.
, 00:44:33:00 ,Your record, attributes, yeah, boom.
, 00:44:33:06
, 00:44:34:11 ,Okay.
, 00:44:36:19 ,We're now with the dealing with vector math.
, 00:44:38:12 ,Take it one step further.
, 00:44:45:13 ,The whole database is a matricy, boom, records people attributes database.
, 00:44:45:22
, 00:44:46:23 ,Cool.
, 00:44:46:29
, 00:44:53:24 ,Again we now can apply matrices math, inversions, and dot products,
, 00:44:58:29 ,all kinds of wonderful things like that.
, 00:45:02:02 ,Actually, so a cosign similarity, measuring the angular differences,
, 00:45:04:18 ,math, math, math, math, math.
, 00:45:12:08
, 00:45:15:27 ,One thing we did was the actual mathematical formula
, 00:45:20:08 ,for the similarity functioning, in case anyone wants to try it
, 00:45:24:25 ,at home or see me after class and we'll discuss it.
, 00:45:25:25 ,Yeah.
, 00:45:28:06 ,Then diagrams, this is really cool.
, 00:45:33:10 ,So to be able to visually to understand and identify overlapping
, 00:45:37:07 ,data sets, you have two at that time sets, A, B,
, 00:45:41:04 ,multiple variables that were in common that were
, 00:45:44:16 ,the same descriptive traits.
, 00:45:47:00 ,Looked at the intersections of them.
, 00:45:47:23
, 00:45:51:21 ,Noted here by these little lines across.
, 00:45:52:21 ,Okay.
, 00:45:57:00 ,So these data sets independent descriptive variables in common.
, 00:46:03:19 ,Then we take those little sections that are in common and we VIN the VINs
, 00:46:05:19 ,as we say.
, 00:46:10:13 ,So take those and watch this.
, 00:46:12:07 ,Bam, bam, bam, bam!
, 00:46:12:17
, 00:46:14:02 ,Look at that.
, 00:46:17:05 ,And then based on that we can actually now actually
, 00:46:21:10 ,the subspace defined by that area is the intersection
, 00:46:25:17 ,of all of these groups and actually identifies records
, 00:46:32:25 ,for which all attributes are identical and actually identifies an actual person.
, 00:46:33:06
, 00:46:36:14 ,(laughter) Wait.
, 00:46:37:24
, 00:46:39:01 ,Okay.
, 00:46:41:00
, 00:46:44:05 ,In summation, the dark side of OSINT.
, 00:46:46:29
, 00:46:50:16 ,So big data, big problem, big data.
, 00:46:54:09 ,Lots of tools are being used for analysis and visualization.
, 00:46:57:24 ,More data sets are being developed and this is the mathematical attacks are
, 00:47:00:23 ,going to become easier and easier.
, 00:47:04:28 ,It's another weapon for social engineering tool kits.
, 00:47:07:01 ,This is information about individuals that we'll able
, 00:47:10:24 ,to ascertain and they're not going to be aware of it.
, 00:47:14:06 ,And they're not voluntarily giving this information, but it's going
, 00:47:17:01 ,to be actually sort of reidentified about them
, 00:47:19:26 ,from these anonymous data sets.
, 00:47:20:10
, 00:47:23:04 ,So cool for us, bad for them.
, 00:47:24:05
, 00:47:27:02 ,What can we do to defend against the dark arts?
, 00:47:27:20
, 00:47:30:29 ,(laughter) Proper sanitization methods.
, 00:47:34:20 ,There are not there's no way there's no standards
, 00:47:42:00 ,to actually implement anonymizing matrix but provide true anonymity.
, 00:47:46:18
, 00:47:48:29 ,We need access controls or my recommendation
, 00:47:52:13 ,is to falsify everything and just make shit up.
, 00:47:54:23 ,So that's what I would do.
, 00:47:58:19 ,(applause) In conclusion yeah.
, 00:48:05:26 ,(applause) SKYDOG: Questions and answers will be handled at the bar.
, 00:48:06:01
, 00:48:07:09 ,You guys are buying!
, 00:48:10:04 , Ladies and gentlemen, the full presentation will be scene
, 00:48:12:28 ,at SkyDog comma little later.
, 00:48:14:19
, 00:48:19:17 ,Thank you for the speaker for letting us go a little long.
, 00:48:23:24 , How can we take out SkyDog and his buddy?
, 00:48:24:18 , Head to the chillout cafe for question and answer
, 00:48:26:14 ,and more elucidation on the slides.
, 00:48:31:14