1 00:00:01,000 --> 00:00:04,320 Speaker 1: From UFOs two ghosts and government cover ups. History is 2 00:00:04,400 --> 00:00:08,000 Speaker 1: riddled with unexplained events. You can turn back now or 3 00:00:08,080 --> 00:00:17,360 Speaker 1: learn the stuff they don't want you to now. All right, Hello, 4 00:00:17,520 --> 00:00:20,760 Speaker 1: welcome to the show. If that intro by our super 5 00:00:20,800 --> 00:00:23,720 Speaker 1: producer Noel was familiar, then that means you are in 6 00:00:23,800 --> 00:00:26,640 Speaker 1: the right place. Ladies and gentlemen. This is stuff they 7 00:00:26,680 --> 00:00:31,160 Speaker 1: don't want you to know. I'm Ben. You're probably wondering 8 00:00:31,280 --> 00:00:35,519 Speaker 1: where Matt is. He is off on an adventure that 9 00:00:35,880 --> 00:00:38,040 Speaker 1: maybe we'll get to in the course of this show. 10 00:00:38,120 --> 00:00:41,560 Speaker 1: But in place of Matt, today we have not one, 11 00:00:42,120 --> 00:00:47,280 Speaker 1: but two very special guests from tech Stuff, from brain Stuff, 12 00:00:47,520 --> 00:00:50,280 Speaker 1: from Forward Thinking. I don't even know how you guys 13 00:00:50,280 --> 00:00:53,400 Speaker 1: found time to come on this show, Jonathan Strickland and 14 00:00:53,520 --> 00:00:57,400 Speaker 1: Lauren vogelbaumb We found time because we literally just recorded 15 00:00:57,440 --> 00:01:01,040 Speaker 1: another Tech Stuff episode with you, like like ten minutes ago. 16 00:01:01,320 --> 00:01:03,400 Speaker 1: Thanks for not giving away our time travel scheme. That 17 00:01:03,440 --> 00:01:08,119 Speaker 1: was good. I'm glad that we have got that out 18 00:01:08,120 --> 00:01:10,360 Speaker 1: of the bag out of the bag Man, which is 19 00:01:10,360 --> 00:01:13,480 Speaker 1: only gonna be funny if you listen to the other episode, right, 20 00:01:13,520 --> 00:01:18,480 Speaker 1: because this is a sequel that's a compacit a sister episode. 21 00:01:18,760 --> 00:01:22,520 Speaker 1: It is. It is now. You guys were kind enough 22 00:01:22,640 --> 00:01:24,680 Speaker 1: or add poor enough taste to have me on your 23 00:01:24,720 --> 00:01:28,160 Speaker 1: show just a few minutes ago when we recorded a 24 00:01:28,200 --> 00:01:33,080 Speaker 1: great episode on how to serve the Web anonymously or 25 00:01:33,080 --> 00:01:37,720 Speaker 1: whether it's even possible. And this was us to something 26 00:01:37,760 --> 00:01:41,039 Speaker 1: that we thought was right up the stuff they don't 27 00:01:41,040 --> 00:01:44,280 Speaker 1: want you to know, Ali, because listeners out there, now, 28 00:01:44,640 --> 00:01:48,440 Speaker 1: you guys know as well as everybody else does now 29 00:01:48,840 --> 00:01:52,240 Speaker 1: that it turns out the United States government in particular 30 00:01:52,760 --> 00:01:56,040 Speaker 1: was putting a lot more energy into tracking people than 31 00:01:56,080 --> 00:01:58,920 Speaker 1: we had all thought. Yeah, I mean, even if you 32 00:01:59,000 --> 00:02:01,880 Speaker 1: take it at face value, where you know you have 33 00:02:02,000 --> 00:02:04,640 Speaker 1: this this system specifically the n s A we're talking 34 00:02:04,640 --> 00:02:08,720 Speaker 1: about here, with the prison specifically with yes, that if 35 00:02:08,760 --> 00:02:10,320 Speaker 1: even if you take it at face value, that what 36 00:02:10,440 --> 00:02:13,440 Speaker 1: they were looking for were foreign agents. So if you 37 00:02:13,480 --> 00:02:18,320 Speaker 1: are a United States citizen, then in theory you would 38 00:02:18,320 --> 00:02:21,080 Speaker 1: not a United States citizen who is not involved in 39 00:02:21,120 --> 00:02:23,280 Speaker 1: one of these schemes, You would not be considered a 40 00:02:23,280 --> 00:02:27,960 Speaker 1: foreign agent and therefore not part of the surveillance. Even 41 00:02:27,960 --> 00:02:31,800 Speaker 1: if you take that at face value, they would look 42 00:02:32,000 --> 00:02:35,200 Speaker 1: for people who they did identify as foreign agents, and 43 00:02:35,240 --> 00:02:38,679 Speaker 1: the people that that those folks talked to, right, Yeah, 44 00:02:38,720 --> 00:02:41,280 Speaker 1: they played the Kevin Bacon game. Yeah. And if you've 45 00:02:41,320 --> 00:02:44,560 Speaker 1: ever played the Kevin Bacon game, you know that it 46 00:02:45,240 --> 00:02:48,440 Speaker 1: gets pretty easy to start linking two people you never 47 00:02:48,440 --> 00:02:50,680 Speaker 1: thought would have had a connection to each other together. 48 00:02:51,520 --> 00:02:56,200 Speaker 1: It doesn't really take that many levels of separation, right, Right. 49 00:02:56,240 --> 00:02:59,880 Speaker 1: And furthermore, they're really collecting information on everyone and sort 50 00:03:00,200 --> 00:03:02,560 Speaker 1: through it in order to find these specific people. But 51 00:03:02,600 --> 00:03:05,560 Speaker 1: the fact that they're collecting and storing this information about 52 00:03:05,600 --> 00:03:10,359 Speaker 1: all of us, it's creepy. It's problematic. So again, even 53 00:03:10,440 --> 00:03:14,600 Speaker 1: taken on face value, it's it's it's troublesome. And then 54 00:03:14,600 --> 00:03:19,680 Speaker 1: when you realize the actual methodology, it becomes downright concerning. 55 00:03:19,720 --> 00:03:22,400 Speaker 1: I mean it's not just that, oh, there's some issues 56 00:03:22,440 --> 00:03:27,679 Speaker 1: here from a technological perspective, the very methodology is problematic. 57 00:03:28,120 --> 00:03:30,880 Speaker 1: I think that's well said. So one of my first 58 00:03:30,919 --> 00:03:34,920 Speaker 1: questions for you guys before you really get into this is, uh, 59 00:03:35,120 --> 00:03:39,080 Speaker 1: if let's say twenty years ago, if someone had come 60 00:03:39,160 --> 00:03:43,360 Speaker 1: up to you in and said the government is watching 61 00:03:43,520 --> 00:03:46,480 Speaker 1: everything and it's just going to watch more, would you 62 00:03:46,520 --> 00:03:49,000 Speaker 1: have would you have thought it was sort of conspiracy 63 00:03:49,080 --> 00:03:53,280 Speaker 1: bunk or would you have thought there was a possibility 64 00:03:53,280 --> 00:03:57,120 Speaker 1: of it happening, I would have thought bunk for sure. Yeah, 65 00:03:57,400 --> 00:04:00,760 Speaker 1: and it's and it's largely because uh well, I mean 66 00:04:01,000 --> 00:04:05,800 Speaker 1: ninety four wasn't there at Yeah. When you're talking about 67 00:04:05,920 --> 00:04:11,280 Speaker 1: how much data gets created or transferred or copied or 68 00:04:11,360 --> 00:04:14,120 Speaker 1: transmitted however you want to look at it, it's an 69 00:04:14,160 --> 00:04:17,719 Speaker 1: astronomical number. It is an enormous number. Just let's just 70 00:04:17,800 --> 00:04:22,960 Speaker 1: take YouTube as the example of data creation. Okay, so 71 00:04:23,000 --> 00:04:26,599 Speaker 1: on YouTube, every single minute that passes, more than a 72 00:04:26,680 --> 00:04:30,280 Speaker 1: hundred hours of video footage is being uploaded to YouTube. 73 00:04:30,640 --> 00:04:33,400 Speaker 1: Some of that's duplicate footage, but it doesn't really matter. 74 00:04:33,560 --> 00:04:36,880 Speaker 1: That's a huge amount of information. Now, just kind of extrapolate. 75 00:04:37,040 --> 00:04:41,200 Speaker 1: Imagine that the entire Internet, this network of computer networks, 76 00:04:41,960 --> 00:04:44,640 Speaker 1: is filled with people who are either creating information or 77 00:04:44,680 --> 00:04:48,120 Speaker 1: accessing it in some way. And the accessing of information 78 00:04:48,200 --> 00:04:52,440 Speaker 1: does in itself create a certain amount of meta data. 79 00:04:52,720 --> 00:04:56,640 Speaker 1: There's information about who, who accessed what and when. This 80 00:04:56,720 --> 00:04:59,039 Speaker 1: is such an enormous amount that most of us would 81 00:04:59,040 --> 00:05:02,960 Speaker 1: think that being able to to capture it and filter 82 00:05:03,040 --> 00:05:05,359 Speaker 1: it and make any meaning of it would be a 83 00:05:05,400 --> 00:05:09,640 Speaker 1: gargentean task beyond our capabilities. But it is a gargentean task. 84 00:05:09,680 --> 00:05:12,440 Speaker 1: But it's not, as it turns out, beyond our capability exactly. 85 00:05:12,520 --> 00:05:16,599 Speaker 1: It turns out that our our technology is sufficiently sophisticated 86 00:05:16,680 --> 00:05:19,280 Speaker 1: enough to be able to weed through that kind of stuff, 87 00:05:19,440 --> 00:05:21,400 Speaker 1: or at least we're working very hard on it. I mean, 88 00:05:21,440 --> 00:05:23,520 Speaker 1: not the three of us at this table, but I'm 89 00:05:23,520 --> 00:05:27,120 Speaker 1: personally aware of well. And and Google is a perfect 90 00:05:27,120 --> 00:05:31,160 Speaker 1: example of how we should have really kind of re 91 00:05:31,360 --> 00:05:34,839 Speaker 1: evaluated this idea that it's just too big, right, because 92 00:05:34,839 --> 00:05:39,599 Speaker 1: Google has created a business that, or at least the 93 00:05:39,600 --> 00:05:42,719 Speaker 1: forward facing part of their business that that's the search engine. 94 00:05:43,080 --> 00:05:45,360 Speaker 1: You know, arguably you would say that the real business 95 00:05:45,400 --> 00:05:48,040 Speaker 1: is advertising, but but the search engine is the thing, 96 00:05:48,120 --> 00:05:50,799 Speaker 1: the product that we're all familiar with, and it's really 97 00:05:50,800 --> 00:05:53,240 Speaker 1: really good. It's a really good search engine if you're 98 00:05:53,240 --> 00:05:57,280 Speaker 1: trying to find something specific. And once we realize how 99 00:05:57,360 --> 00:06:00,520 Speaker 1: much information is out there and how Google has developed 100 00:06:00,520 --> 00:06:04,000 Speaker 1: an algorithm that can effectively find the stuff you actually 101 00:06:04,040 --> 00:06:07,159 Speaker 1: want to see, then you start to realize, oh, I 102 00:06:07,200 --> 00:06:10,240 Speaker 1: guess it really is possible to build in that a 103 00:06:10,360 --> 00:06:14,160 Speaker 1: similar kind of tool that could be useful if you're 104 00:06:14,200 --> 00:06:18,640 Speaker 1: looking for signs of activity. Uh, if you happen to 105 00:06:18,680 --> 00:06:22,159 Speaker 1: be an enormous government organization that is in charge of 106 00:06:22,360 --> 00:06:26,719 Speaker 1: discovering cryptic messages set between foreign agents that could potentially 107 00:06:26,720 --> 00:06:30,240 Speaker 1: affect your country, or friends of foreign agents, yes, or 108 00:06:30,360 --> 00:06:33,320 Speaker 1: people who know friends of foreign agents. Yes. That's the 109 00:06:33,320 --> 00:06:36,040 Speaker 1: problem is that this this ripples outward. Right, if if 110 00:06:36,080 --> 00:06:39,479 Speaker 1: it were just the foreign agents, that becomes an issue 111 00:06:39,480 --> 00:06:42,000 Speaker 1: because then you have to be able to to reliably 112 00:06:42,120 --> 00:06:44,719 Speaker 1: identify a foreign agent versus someone who is not a 113 00:06:44,760 --> 00:06:47,800 Speaker 1: foreign agent. But then if you go one ripple outward, 114 00:06:47,839 --> 00:06:50,680 Speaker 1: who are these people talking to and why? Well, I 115 00:06:50,760 --> 00:06:52,719 Speaker 1: can understand why you would be interested in that, but 116 00:06:52,760 --> 00:06:55,320 Speaker 1: they're going to be people in that that one ripple 117 00:06:55,360 --> 00:06:58,880 Speaker 1: outward who are not in any way connected with anything nefarious, 118 00:06:58,880 --> 00:07:02,080 Speaker 1: but they're going to get swept up in that surveillance. Anyway, 119 00:07:02,400 --> 00:07:05,240 Speaker 1: if you go a further ring out, go for it. 120 00:07:05,400 --> 00:07:08,880 Speaker 1: That's an enormous number of people. This is what Facebook 121 00:07:08,960 --> 00:07:12,880 Speaker 1: is completely built upon, the whole idea of that social network. 122 00:07:13,560 --> 00:07:15,880 Speaker 1: If you ever see any of their presentations, you just 123 00:07:15,960 --> 00:07:19,880 Speaker 1: see how this this this one small group of people 124 00:07:19,880 --> 00:07:24,000 Speaker 1: who have these interconnections between each other become this enormous 125 00:07:24,320 --> 00:07:26,520 Speaker 1: mass of people. When you just go out a couple 126 00:07:26,560 --> 00:07:30,840 Speaker 1: of steps. Yet now this I'm glad that we're talking 127 00:07:30,840 --> 00:07:33,240 Speaker 1: about this because this is something that escapes a lot 128 00:07:33,240 --> 00:07:37,000 Speaker 1: of people that the uh, the government agency, the n 129 00:07:37,120 --> 00:07:42,920 Speaker 1: s A has automated the collection and analysis of this 130 00:07:43,360 --> 00:07:45,480 Speaker 1: of this stuff for people who are concerned. You know, 131 00:07:45,520 --> 00:07:48,000 Speaker 1: the n s A is listening in on my phone 132 00:07:48,040 --> 00:07:51,360 Speaker 1: calls and reading my emails. They're taking your phone calls 133 00:07:51,520 --> 00:07:54,680 Speaker 1: and emails and they're keeping them like like you said, Lauren, 134 00:07:55,000 --> 00:07:57,080 Speaker 1: And what they'll do is if you pop up on 135 00:07:57,120 --> 00:08:00,240 Speaker 1: a different algorhythm, than they'll actually have a human. Like 136 00:08:00,280 --> 00:08:04,480 Speaker 1: any other big organization from a call center to the FBI, 137 00:08:04,640 --> 00:08:06,960 Speaker 1: it's kind of difficult to actually get to a human. 138 00:08:07,000 --> 00:08:10,200 Speaker 1: You have to go through a few steps. Yeah, dial one, 139 00:08:10,560 --> 00:08:13,920 Speaker 1: wait for stuff. You listen to a robot voice and 140 00:08:13,960 --> 00:08:16,200 Speaker 1: I hate it, just a side note, I hate it 141 00:08:16,240 --> 00:08:19,840 Speaker 1: when the the automated line won't let you just push 142 00:08:19,840 --> 00:08:22,280 Speaker 1: a button and you have to stand there wherever you 143 00:08:22,320 --> 00:08:30,480 Speaker 1: are and look really stupid and go operator operate. Yes, yes, yes, yea, 144 00:08:30,920 --> 00:08:33,760 Speaker 1: And everybody's so solemn. We all just said the same yes. 145 00:08:34,000 --> 00:08:38,679 Speaker 1: But um. But but moving from this, we before we 146 00:08:38,720 --> 00:08:41,320 Speaker 1: go too deep on the government, we should make a 147 00:08:41,360 --> 00:08:44,160 Speaker 1: point that they're not the only people looking at web 148 00:08:44,200 --> 00:08:48,679 Speaker 1: activity when we talk about yeah, I mean, companies obviously 149 00:08:48,880 --> 00:08:51,040 Speaker 1: are looking at a lot of web activity as well, 150 00:08:51,280 --> 00:08:55,240 Speaker 1: for multiple reasons. Uh. Mostly companies are looking at activity 151 00:08:55,280 --> 00:08:57,079 Speaker 1: in order to make more money, all right. They want 152 00:08:57,120 --> 00:08:59,040 Speaker 1: to be able to sell you stuff better, or to 153 00:08:59,160 --> 00:09:02,440 Speaker 1: sell you as a consumer to an advertising company so 154 00:09:02,559 --> 00:09:05,560 Speaker 1: that that advertising company can tell you stuff better. Yeah. Yeah, 155 00:09:05,679 --> 00:09:08,240 Speaker 1: it all comes down to, you know, where are the dollars, 156 00:09:08,600 --> 00:09:10,440 Speaker 1: where are they coming from, and where are they going to? 157 00:09:10,600 --> 00:09:14,719 Speaker 1: And so we the users end up playing a big 158 00:09:14,800 --> 00:09:16,520 Speaker 1: role in that. I mean we we are the ones 159 00:09:16,559 --> 00:09:20,640 Speaker 1: who generate revenue for companies. And you know, if it 160 00:09:20,679 --> 00:09:23,120 Speaker 1: weren't that way, the Internet would be a very different place. 161 00:09:23,200 --> 00:09:25,640 Speaker 1: For one thing, it would not be nearly as robust 162 00:09:25,679 --> 00:09:27,959 Speaker 1: as it is, right, it would it would be mainly 163 00:09:28,400 --> 00:09:32,800 Speaker 1: limited to communication lines between things like research institutes and 164 00:09:32,840 --> 00:09:36,480 Speaker 1: the government, which, big surprise, that's what originally the Internet 165 00:09:36,520 --> 00:09:40,920 Speaker 1: was all about. Our yeah, our ponnett was essentially connections 166 00:09:41,000 --> 00:09:46,360 Speaker 1: between scientific research institutes, universities, and government installations. And that 167 00:09:46,440 --> 00:09:49,199 Speaker 1: was it, and and that was because you know, that 168 00:09:49,280 --> 00:09:51,200 Speaker 1: was what it was built for, was built to be 169 00:09:51,320 --> 00:09:55,760 Speaker 1: this really fast communications and networking ability so that people 170 00:09:55,760 --> 00:10:00,160 Speaker 1: could share things very effectively. What it's grown into is 171 00:10:00,200 --> 00:10:05,120 Speaker 1: this crazy world that melds things like entertainment and commerce 172 00:10:05,200 --> 00:10:09,040 Speaker 1: and communications all into one big package, which in many 173 00:10:09,040 --> 00:10:13,080 Speaker 1: ways is legitimately awesome. Obviously, we wouldn't have jobs if 174 00:10:13,080 --> 00:10:15,120 Speaker 1: there were an internet, or at least we wouldn't have 175 00:10:15,160 --> 00:10:19,280 Speaker 1: these jobs we are on the internet right now. Yeah, well, 176 00:10:19,400 --> 00:10:23,240 Speaker 1: you guys get paid. I'm here for community service. Your 177 00:10:23,320 --> 00:10:26,440 Speaker 1: internship has lasted longer than any other I've ever seen. 178 00:10:26,600 --> 00:10:28,640 Speaker 1: That's true. Hey, will you sign off for my hours 179 00:10:28,720 --> 00:10:33,440 Speaker 1: before this is? Thanks guys. UM. One question, though, that 180 00:10:33,520 --> 00:10:38,000 Speaker 1: a lot of people will have is the following. Just 181 00:10:38,000 --> 00:10:40,560 Speaker 1: a little bit of background here. UM, The question is 182 00:10:40,559 --> 00:10:47,000 Speaker 1: is surfing anonymously legal? Uh? The background here is that 183 00:10:47,480 --> 00:10:52,960 Speaker 1: often the desire to surf anonymously is depicted as one 184 00:10:53,000 --> 00:10:57,040 Speaker 1: that is inherently sinister. That is the same sort of 185 00:10:57,559 --> 00:11:01,280 Speaker 1: uh perception that is given two things is like torrents 186 00:11:01,840 --> 00:11:04,960 Speaker 1: now or peer to peer networks. Peer to peer networks 187 00:11:05,240 --> 00:11:09,680 Speaker 1: as they are by themselves, are not sinister or shady 188 00:11:09,880 --> 00:11:12,080 Speaker 1: or illegal, right ap. Peer to peer network is just 189 00:11:12,160 --> 00:11:15,360 Speaker 1: a means of distribution of files right, But when you 190 00:11:15,400 --> 00:11:18,120 Speaker 1: start distributing files that you don't have the right to 191 00:11:18,160 --> 00:11:21,959 Speaker 1: distribute network, that's where the illegal activity comes, and then 192 00:11:22,040 --> 00:11:25,440 Speaker 1: you end up sort of casting this shadow across the 193 00:11:25,559 --> 00:11:29,760 Speaker 1: entire technology. So there are companies like music and movie 194 00:11:29,760 --> 00:11:32,920 Speaker 1: companies that just say peer to peer networks alone are 195 00:11:33,000 --> 00:11:36,720 Speaker 1: bad because those are a lot of the ways that 196 00:11:36,720 --> 00:11:39,920 Speaker 1: that illegal file sharing got spread around, you know, just 197 00:11:40,000 --> 00:11:42,400 Speaker 1: a few years ago. Now it's not even that big 198 00:11:42,440 --> 00:11:44,559 Speaker 1: of a deal because you can get pretty much everything 199 00:11:45,800 --> 00:11:49,240 Speaker 1: in a billion different places. But uh, you know, if 200 00:11:49,280 --> 00:11:52,319 Speaker 1: you're talking about just trying to serve anonymously, it all 201 00:11:52,360 --> 00:11:55,440 Speaker 1: depends upon where you are in the world. In the 202 00:11:55,520 --> 00:11:59,080 Speaker 1: United States, it's not a big deal. There's no law 203 00:11:59,320 --> 00:12:01,880 Speaker 1: that you'd be very king by trying to hide your 204 00:12:01,920 --> 00:12:04,880 Speaker 1: IP address. The laws that you would be breaking would 205 00:12:04,880 --> 00:12:07,600 Speaker 1: be if you tried to do anything illegal while you 206 00:12:07,640 --> 00:12:09,880 Speaker 1: were doing that, or even if you weren't, whether or 207 00:12:09,880 --> 00:12:11,920 Speaker 1: not you're trying to hide what you're doing. If you're 208 00:12:11,920 --> 00:12:15,320 Speaker 1: doing something illegal, that's against the law, right, Yeah, Like 209 00:12:15,320 --> 00:12:17,280 Speaker 1: like you like you don't have to use tour to 210 00:12:17,360 --> 00:12:19,560 Speaker 1: buy drugs. There are many other reasons that you can 211 00:12:19,640 --> 00:12:21,920 Speaker 1: use tor Yeah, but if you do buy drugs. That's 212 00:12:21,920 --> 00:12:24,720 Speaker 1: against the law, and then if you're caught, you could 213 00:12:24,760 --> 00:12:27,040 Speaker 1: be punished for it, or you will be if you're caught. 214 00:12:27,480 --> 00:12:30,120 Speaker 1: I see. Yeah. So so here in the West and 215 00:12:30,160 --> 00:12:33,760 Speaker 1: the United States, Canada and so on, there is not 216 00:12:33,920 --> 00:12:38,000 Speaker 1: a law against uh surfing the web anonymously. I don't 217 00:12:38,040 --> 00:12:41,600 Speaker 1: I don't think it's even against the law to uh 218 00:12:41,760 --> 00:12:44,400 Speaker 1: go into some of the deep web stuff to which 219 00:12:44,440 --> 00:12:47,079 Speaker 1: we alluded um, which you guys have covered in a 220 00:12:47,160 --> 00:12:49,200 Speaker 1: previous episode and Matt and I have covered as well. 221 00:12:49,679 --> 00:12:52,240 Speaker 1: We're talking about this Silk Road and and not the 222 00:12:52,320 --> 00:12:55,880 Speaker 1: historic one through Central Asia. But as you said, other 223 00:12:55,960 --> 00:12:59,440 Speaker 1: countries have different perspectives, like you've got a great example 224 00:12:59,440 --> 00:13:03,360 Speaker 1: about China. Sure, yeah, China. They have a program in 225 00:13:03,440 --> 00:13:07,560 Speaker 1: China that is called the Golden Shield Project, also more 226 00:13:07,600 --> 00:13:11,480 Speaker 1: commonly known as the Great Firewall of China. UH. And 227 00:13:11,559 --> 00:13:13,800 Speaker 1: the reason for this is that it said effort i'll 228 00:13:13,920 --> 00:13:17,960 Speaker 1: um on part of the Chinese government to censor and 229 00:13:17,960 --> 00:13:21,640 Speaker 1: and have surveillance over Internet activities within China, so that 230 00:13:22,160 --> 00:13:26,000 Speaker 1: the main purpose of it is to prevent objectionable material 231 00:13:26,559 --> 00:13:30,040 Speaker 1: as defined by the Chinese government from getting to Chinese 232 00:13:30,040 --> 00:13:34,280 Speaker 1: citizens using the internet. So flat out block some websites 233 00:13:34,840 --> 00:13:38,840 Speaker 1: and some search terms and like some yeah things like Facebook. 234 00:13:38,880 --> 00:13:42,120 Speaker 1: You can't access Facebook in China using if you were 235 00:13:42,160 --> 00:13:44,240 Speaker 1: just trying to connect to a Chinese I s P 236 00:13:44,840 --> 00:13:49,320 Speaker 1: and go through methods domain name server. So if you 237 00:13:49,360 --> 00:13:52,240 Speaker 1: were to if you were to just use like plug 238 00:13:52,240 --> 00:13:54,679 Speaker 1: and play, you're you're just trying to use your browser 239 00:13:54,760 --> 00:13:57,040 Speaker 1: to get to certain places. You would find out that 240 00:13:57,040 --> 00:14:00,920 Speaker 1: there's some websites you just cannot access that way in China. 241 00:14:01,480 --> 00:14:04,199 Speaker 1: In order to access those websites, you have to circumvent 242 00:14:04,559 --> 00:14:07,959 Speaker 1: the protections that have been put in place. UH. In general, 243 00:14:08,320 --> 00:14:12,040 Speaker 1: this is not seen as a huge deal, right, It's 244 00:14:12,080 --> 00:14:15,200 Speaker 1: it's it's it's frowned upon. You're not supposed to do it. 245 00:14:15,240 --> 00:14:18,640 Speaker 1: But as far as I am aware, no one has 246 00:14:18,679 --> 00:14:25,040 Speaker 1: been UH persecuted and or prosecuted for trying to circumnavigate 247 00:14:25,080 --> 00:14:27,520 Speaker 1: the firewall of China. However, if you were to do 248 00:14:27,600 --> 00:14:32,880 Speaker 1: something such as post messages that are anti Chinese government 249 00:14:33,040 --> 00:14:37,320 Speaker 1: to websites, then that is very much considered against the law, 250 00:14:37,360 --> 00:14:40,240 Speaker 1: and you will they will look for you, and if 251 00:14:40,240 --> 00:14:43,320 Speaker 1: they find you and catch you, they will punish you. Right, 252 00:14:43,400 --> 00:14:47,080 Speaker 1: and it might also be used as perhaps a pretext 253 00:14:47,200 --> 00:14:50,520 Speaker 1: for arresting someone, at least in that country, kind of 254 00:14:50,560 --> 00:14:53,280 Speaker 1: the same way the tax evasion was the crime for 255 00:14:53,320 --> 00:14:57,200 Speaker 1: which Capone was ultimately arrested. Yeah. That in some cases, 256 00:14:57,240 --> 00:15:00,600 Speaker 1: depending again upon what country you are in, uh, this 257 00:15:00,720 --> 00:15:03,880 Speaker 1: might be the the door that opens up so that 258 00:15:03,920 --> 00:15:05,640 Speaker 1: they can get you for what they really want you for. 259 00:15:06,040 --> 00:15:10,200 Speaker 1: Right And and when we say we're not especially picking 260 00:15:10,240 --> 00:15:12,680 Speaker 1: on China, oh, although I do have to say, there's 261 00:15:12,720 --> 00:15:16,440 Speaker 1: one really cool thing that that freaks me out a bit. 262 00:15:16,480 --> 00:15:20,040 Speaker 1: And if you are on just the regular you know, 263 00:15:20,080 --> 00:15:22,960 Speaker 1: the version of the inner State uh Internet in China. 264 00:15:23,400 --> 00:15:27,520 Speaker 1: Every so often these two cartoon police characters will pop 265 00:15:27,600 --> 00:15:30,240 Speaker 1: up on the screen just to let you know that 266 00:15:30,280 --> 00:15:34,280 Speaker 1: they're they're looking out for you, they're protecting you. They're 267 00:15:34,280 --> 00:15:37,600 Speaker 1: protecting and serving by making sure you're not doing anything wrong. Actually, 268 00:15:37,720 --> 00:15:40,240 Speaker 1: what they're doing is they're protecting you by making sure 269 00:15:40,240 --> 00:15:43,800 Speaker 1: all that terrible information that would flood flood your browser 270 00:15:44,000 --> 00:15:47,240 Speaker 1: if if it only had the chance, because they know 271 00:15:47,480 --> 00:15:50,360 Speaker 1: you are an upstanding Chinese citizen and would never try 272 00:15:50,400 --> 00:15:52,680 Speaker 1: to access that kind of stuff. But that stuff is 273 00:15:52,720 --> 00:15:54,840 Speaker 1: trying to get it you no matter what. And those 274 00:15:54,920 --> 00:15:56,880 Speaker 1: cops they are making sure that you are going to 275 00:15:56,920 --> 00:15:58,960 Speaker 1: be safe from it. They also have blue eyes, which 276 00:15:59,040 --> 00:16:01,040 Speaker 1: is very weird. Their names of Chinging and Cha Cha. 277 00:16:01,280 --> 00:16:04,840 Speaker 1: It's based on a pun that means police in Chinese. 278 00:16:04,880 --> 00:16:07,480 Speaker 1: So check check it out and google it if you 279 00:16:07,520 --> 00:16:10,080 Speaker 1: have a chance. Just remember that China will know you 280 00:16:10,200 --> 00:16:15,920 Speaker 1: looked at it. And uh so earlier, Um, we we 281 00:16:16,000 --> 00:16:20,680 Speaker 1: mentioned toward the onion router, right, and uh what I 282 00:16:20,720 --> 00:16:24,280 Speaker 1: wanted to ask about is if you could, because you 283 00:16:24,280 --> 00:16:27,640 Speaker 1: guys are the experts here on technical matters, if you 284 00:16:27,680 --> 00:16:31,800 Speaker 1: could outline briefly for our listeners, what's the difference between 285 00:16:31,960 --> 00:16:36,480 Speaker 1: like a privacy mode on a browser and something like tour. 286 00:16:37,080 --> 00:16:40,600 Speaker 1: That's a great question. Yeah, in brief, a privacy browser 287 00:16:41,720 --> 00:16:46,080 Speaker 1: on your home computer does absolutely nothing to to protect 288 00:16:46,120 --> 00:16:48,520 Speaker 1: what you are doing from anyone aside from someone who 289 00:16:48,600 --> 00:16:51,920 Speaker 1: is looking purely at your home computer. Yeah, exactly, the 290 00:16:52,480 --> 00:16:56,240 Speaker 1: text mat real quick. While you're doing that, I'll continue 291 00:16:56,240 --> 00:16:58,920 Speaker 1: to explain. So, yeah, the privacy mode, what it's doing 292 00:16:59,000 --> 00:17:02,360 Speaker 1: is it's preventing stuff that would normally show up and say, 293 00:17:02,400 --> 00:17:06,360 Speaker 1: your search history, your browsing history cookies, is preventing all 294 00:17:06,359 --> 00:17:08,639 Speaker 1: that kind of stuff from happening so that someone who 295 00:17:08,680 --> 00:17:11,280 Speaker 1: gets access to your machine. Can't just look and see 296 00:17:11,280 --> 00:17:13,920 Speaker 1: what it is you've been up to. However, anyone who 297 00:17:13,920 --> 00:17:17,919 Speaker 1: can see the traffic that's going across your local network 298 00:17:18,280 --> 00:17:21,040 Speaker 1: that includes perhaps other machines that are also on the 299 00:17:21,080 --> 00:17:24,720 Speaker 1: local network, your router, the modem, your I s P, 300 00:17:25,800 --> 00:17:29,240 Speaker 1: all of these entities know exactly what you're doing because 301 00:17:29,280 --> 00:17:31,639 Speaker 1: in order for you to get the stuff you're trying 302 00:17:31,680 --> 00:17:35,399 Speaker 1: to get, they these entities have to know where to 303 00:17:35,480 --> 00:17:37,800 Speaker 1: send it, right right, This is so you know, up 304 00:17:37,840 --> 00:17:39,960 Speaker 1: to and including the website that you're accessing, they know 305 00:17:40,000 --> 00:17:42,159 Speaker 1: who you are as well, right, Yeah, at least they 306 00:17:42,160 --> 00:17:44,520 Speaker 1: know the I P edge, yes, and they know they 307 00:17:44,560 --> 00:17:47,640 Speaker 1: know what network it's going to. So really it's it's 308 00:17:47,720 --> 00:17:51,320 Speaker 1: you know, you can't hide your IP address perfectly because 309 00:17:51,320 --> 00:17:54,359 Speaker 1: if you did, no information would ever come back to 310 00:17:54,440 --> 00:17:58,680 Speaker 1: your computer. Now we, uh, we do have an interesting 311 00:17:59,040 --> 00:18:02,560 Speaker 1: fact here, and by interesting I mean disturbing. So I'll 312 00:18:02,600 --> 00:18:06,159 Speaker 1: just go ahead and ask how much information does someone 313 00:18:06,400 --> 00:18:09,240 Speaker 1: a company or a government or whomever need about you 314 00:18:09,320 --> 00:18:11,560 Speaker 1: before they can figure out who you are? All right? 315 00:18:12,480 --> 00:18:15,600 Speaker 1: This is a kind of fascinating. Did you ever hear 316 00:18:15,640 --> 00:18:21,120 Speaker 1: the story about how Target had identified a customer as 317 00:18:21,200 --> 00:18:24,040 Speaker 1: being pregnant. It was It turned out to be a 318 00:18:24,080 --> 00:18:28,959 Speaker 1: young lady, teenager, and so Target pregnant people are ladies, 319 00:18:29,440 --> 00:18:32,400 Speaker 1: that that would be true. That is true, Lauren, thank 320 00:18:32,440 --> 00:18:37,840 Speaker 1: you is a good point. A young lady pregnant, Uh, 321 00:18:38,200 --> 00:18:41,040 Speaker 1: Harris don't know. Parents don't know, parents don't know. She 322 00:18:41,080 --> 00:18:45,280 Speaker 1: has not told them. Uh. And Target starts proactively sending 323 00:18:45,400 --> 00:18:49,840 Speaker 1: her offers for things that a pregnant lady wouldn't possibly need. 324 00:18:50,640 --> 00:18:55,280 Speaker 1: And her father found the offers and got very upset, 325 00:18:55,359 --> 00:18:59,399 Speaker 1: saying like, why is Target sending this unsolit solicited stuff? 326 00:18:59,400 --> 00:19:01,480 Speaker 1: What do you what are you saying about? My daughter? 327 00:19:02,080 --> 00:19:05,320 Speaker 1: Raised a big fuss about it. You have offended mod dignity. 328 00:19:05,440 --> 00:19:07,680 Speaker 1: That's how I pickture him speaking. Yes, it was a 329 00:19:07,720 --> 00:19:12,560 Speaker 1: Southern gentleman from the fun city of Savannah there with 330 00:19:12,560 --> 00:19:14,920 Speaker 1: a white glove and just slapped the front door. Yeah. 331 00:19:15,160 --> 00:19:19,119 Speaker 1: Challenge you, I challenge your entire organization to Yeah. No, 332 00:19:19,320 --> 00:19:21,320 Speaker 1: that's not exactly what happened. But he did raise a 333 00:19:21,359 --> 00:19:25,040 Speaker 1: fuss and then later wrote a second a follow up 334 00:19:25,080 --> 00:19:28,320 Speaker 1: message saying I had a talk with my daughter. It 335 00:19:28,400 --> 00:19:30,359 Speaker 1: turns out that I did not. I was not aware 336 00:19:30,400 --> 00:19:34,040 Speaker 1: that she was pregnant. But this raised the point of 337 00:19:34,240 --> 00:19:37,800 Speaker 1: how did target know? What was it that gave target 338 00:19:37,840 --> 00:19:40,800 Speaker 1: the information? How did they predict this? And as it 339 00:19:40,840 --> 00:19:43,440 Speaker 1: turned out, it had the company had been watching her 340 00:19:43,480 --> 00:19:48,560 Speaker 1: purchase patterns and determined that statistically speaking, she was very 341 00:19:48,640 --> 00:19:53,480 Speaker 1: likely pregnant. And so this is an illustration that you 342 00:19:53,520 --> 00:19:57,400 Speaker 1: don't have to have actively shared some information about yourself 343 00:19:57,840 --> 00:20:01,680 Speaker 1: for an entity or a per son to draw conclusions 344 00:20:01,760 --> 00:20:06,600 Speaker 1: about at least your what your physical state is, or 345 00:20:06,640 --> 00:20:09,639 Speaker 1: what your your state of mind might be. Even if 346 00:20:09,680 --> 00:20:13,159 Speaker 1: it's not your specific name and identity, it could be 347 00:20:13,280 --> 00:20:15,960 Speaker 1: enough to be able to single out who you are 348 00:20:16,520 --> 00:20:19,840 Speaker 1: from a level that's separate from my name is Jonathan 349 00:20:19,840 --> 00:20:24,600 Speaker 1: Strickland and I live in Atlanta, Right, That actually would 350 00:20:24,600 --> 00:20:29,040 Speaker 1: be very easy. They're probably very few Joan Strickland's living 351 00:20:29,040 --> 00:20:30,840 Speaker 1: in Atlanta. There might be a few, because you know, 352 00:20:30,880 --> 00:20:33,440 Speaker 1: there are a lot of other Jonathan Strickland's. Lauren vocal 353 00:20:33,440 --> 00:20:37,520 Speaker 1: Bam might be the easiest to paying directly to zoom 354 00:20:37,520 --> 00:20:41,120 Speaker 1: in pretty quickly. So the real answer to this question, 355 00:20:41,119 --> 00:20:45,600 Speaker 1: according to research specialists, is that thirty three bits of 356 00:20:45,680 --> 00:20:50,160 Speaker 1: information called bits of entropy, and this in this identification 357 00:20:50,240 --> 00:20:53,359 Speaker 1: business are required in order to narrow it down to 358 00:20:53,400 --> 00:20:57,560 Speaker 1: a specific person out of all the people on Earth. 359 00:20:58,240 --> 00:21:00,840 Speaker 1: And and these these bits of information can be anything 360 00:21:00,920 --> 00:21:03,800 Speaker 1: from from your gender to the type of car you drive, 361 00:21:04,160 --> 00:21:07,439 Speaker 1: to your zip code to like, like, it doesn't have 362 00:21:07,520 --> 00:21:10,320 Speaker 1: to be the same thirty three bits in order, it 363 00:21:10,320 --> 00:21:12,880 Speaker 1: could be any thirty three bits, and bit in this 364 00:21:12,920 --> 00:21:17,160 Speaker 1: case means something specific. Like like in the computer world 365 00:21:17,200 --> 00:21:20,800 Speaker 1: we talk about digital Uh, you know these binary digits. 366 00:21:20,840 --> 00:21:22,960 Speaker 1: That that's what a bit is. It's either a zero 367 00:21:23,040 --> 00:21:25,040 Speaker 1: or a one, which you could think of as either 368 00:21:25,080 --> 00:21:30,280 Speaker 1: a no or yes. Well, uh, some bits, some pieces 369 00:21:30,280 --> 00:21:34,520 Speaker 1: of information represent a single bit, like gender is considered 370 00:21:34,520 --> 00:21:38,720 Speaker 1: to be a single bit, putting gender discussions aside. For 371 00:21:38,720 --> 00:21:41,879 Speaker 1: for many people, this would be male or female. All right, 372 00:21:42,119 --> 00:21:46,919 Speaker 1: that that obviously oversimplifies things, but for the purposes for identification, 373 00:21:47,359 --> 00:21:50,680 Speaker 1: male female tends to be uh. The way that they 374 00:21:50,760 --> 00:21:52,640 Speaker 1: look at it, very black and white kind of approach. 375 00:21:53,200 --> 00:21:57,120 Speaker 1: That represents one bit. Something like your zip code might 376 00:21:57,119 --> 00:22:01,800 Speaker 1: be several bits of information that would make up just 377 00:22:01,920 --> 00:22:05,000 Speaker 1: one zip code, but all it takes is thirty three bits. 378 00:22:05,119 --> 00:22:08,600 Speaker 1: Some of those bits might be connected to a larger concept, 379 00:22:08,960 --> 00:22:12,719 Speaker 1: like your model of car, uh, the specific region you 380 00:22:12,760 --> 00:22:15,720 Speaker 1: live in, whatever it is your age. That's another good one. 381 00:22:16,240 --> 00:22:19,240 Speaker 1: But all you need are thirty three bits worth of 382 00:22:19,280 --> 00:22:22,119 Speaker 1: this information to be able to identify. And the reason 383 00:22:22,160 --> 00:22:25,120 Speaker 1: for that is you take this yes or no. That's 384 00:22:25,160 --> 00:22:27,879 Speaker 1: a base of two, right, You've you've got two options. 385 00:22:28,200 --> 00:22:30,800 Speaker 1: You take that too, then you have the thirty three 386 00:22:30,840 --> 00:22:33,919 Speaker 1: different bits. That's two to the power of thirty three. 387 00:22:34,200 --> 00:22:36,320 Speaker 1: If you work that out, that ends up being more 388 00:22:36,400 --> 00:22:39,760 Speaker 1: than eight billion. Two to the thirty third powers more 389 00:22:39,760 --> 00:22:44,200 Speaker 1: than eight billion. There are seven billion people on Earth. Wait, 390 00:22:44,280 --> 00:22:47,399 Speaker 1: we've got made up people in this. It means that 391 00:22:47,440 --> 00:22:50,359 Speaker 1: we have more than enough information to to account for 392 00:22:50,400 --> 00:22:53,720 Speaker 1: the seven billion people who are actually alive. So, uh, 393 00:22:53,760 --> 00:22:56,280 Speaker 1: if you the idea is that with those thirty three bits, 394 00:22:56,320 --> 00:22:59,840 Speaker 1: you can then have enough personal identifiable information to narrow 395 00:22:59,840 --> 00:23:04,960 Speaker 1: it down to a specific individual. And also, it's devilishly 396 00:23:05,280 --> 00:23:10,280 Speaker 1: easy to forget that what you're putting out on the 397 00:23:10,320 --> 00:23:14,120 Speaker 1: internet personally identifies you, right say, all sorts of things 398 00:23:14,119 --> 00:23:18,119 Speaker 1: that they imagine are innocuous. I mean, Twitter is in 399 00:23:18,160 --> 00:23:21,920 Speaker 1: the Congressional Library. Now, yeah, you can get an entire 400 00:23:21,960 --> 00:23:25,560 Speaker 1: you can download an entire Twitter history, which is for 401 00:23:25,640 --> 00:23:28,239 Speaker 1: some of us quite a large file. As it turns out. 402 00:23:28,280 --> 00:23:31,080 Speaker 1: I think I have more than seventeen dozen tweets. So, um, 403 00:23:31,600 --> 00:23:35,480 Speaker 1: I clearly am not as as worried about anonymity as 404 00:23:35,520 --> 00:23:39,840 Speaker 1: some people are. Perhaps that is a foolish thing on 405 00:23:39,880 --> 00:23:43,399 Speaker 1: my part, but uh, there's an interesting example of this 406 00:23:43,480 --> 00:23:46,399 Speaker 1: as well. Researchers at Stanford and the University of Texas. 407 00:23:46,760 --> 00:23:51,480 Speaker 1: We're able to identify Netflix viewers based upon their activity, 408 00:23:51,640 --> 00:23:53,520 Speaker 1: and part of that was because these are the same. 409 00:23:53,680 --> 00:23:57,320 Speaker 1: These viewers would do things like leave reviews for movies 410 00:23:57,320 --> 00:24:00,480 Speaker 1: on other sites and just by looking at the stuff 411 00:24:00,520 --> 00:24:03,680 Speaker 1: that you wouldn't think would personally identify you, right, because 412 00:24:03,720 --> 00:24:06,480 Speaker 1: it's just it's just you your opinion about a movie. 413 00:24:06,520 --> 00:24:11,840 Speaker 1: It's not hey, I happened to be five foot whatever. 414 00:24:12,080 --> 00:24:14,960 Speaker 1: I'm not telling you. Yeah, but they But the point 415 00:24:15,000 --> 00:24:19,000 Speaker 1: being then is that there's uh, there, there's some puzzle 416 00:24:19,359 --> 00:24:25,000 Speaker 1: solving that can happen very easily, right because they say, oh, um, 417 00:24:25,119 --> 00:24:29,959 Speaker 1: anonymous user A watch this thing on Netflix at this time, 418 00:24:30,119 --> 00:24:35,320 Speaker 1: and then oh surprise, shortly thereafter, anonymous user oh wait, 419 00:24:35,359 --> 00:24:38,080 Speaker 1: it's anonymous user A. And they said that this was 420 00:24:38,200 --> 00:24:40,240 Speaker 1: they gave it three stars, and what they did on 421 00:24:40,320 --> 00:24:44,119 Speaker 1: Netflix like like this, this anonymous user A watched a 422 00:24:44,600 --> 00:24:48,200 Speaker 1: uh particular movie at a particular time. This other person 423 00:24:48,200 --> 00:24:52,240 Speaker 1: whose identity we know, left a review on IMDb, and 424 00:24:52,400 --> 00:24:55,400 Speaker 1: based upon the time between these two events, were reasonably 425 00:24:55,440 --> 00:24:57,960 Speaker 1: certain that anonymous user A is this person we know. 426 00:24:58,600 --> 00:25:02,080 Speaker 1: An anonymous user A is completely wrong about Big Trouble 427 00:25:02,119 --> 00:25:05,840 Speaker 1: a Little Chine, which is an amazing movie. It is not. 428 00:25:06,040 --> 00:25:08,280 Speaker 1: It is not a good bad movie. It is a 429 00:25:08,320 --> 00:25:11,120 Speaker 1: good good movie because he's the sidekick the whole time. 430 00:25:11,119 --> 00:25:15,879 Speaker 1: You got pork Shop Express. Come on, So what is 431 00:25:16,280 --> 00:25:19,000 Speaker 1: the Tour project about? You guys have done a you 432 00:25:19,040 --> 00:25:21,800 Speaker 1: guys done a podcast on this. Um, Matt and I 433 00:25:21,880 --> 00:25:24,040 Speaker 1: have done some videos, but we've never done a full 434 00:25:24,080 --> 00:25:27,800 Speaker 1: podcast on it. So it's tour you know, kind of 435 00:25:27,840 --> 00:25:30,280 Speaker 1: stands for the Onion Router. It's really its own name now, 436 00:25:30,359 --> 00:25:33,639 Speaker 1: it's just tour sure. Originally the Onion Router was based 437 00:25:33,680 --> 00:25:37,040 Speaker 1: on the idea that, um, it's encrypting things in layers, yes, 438 00:25:37,359 --> 00:25:40,240 Speaker 1: so that you would go, uh, an information from point 439 00:25:40,280 --> 00:25:43,199 Speaker 1: A to point Z, let's say, would go through all 440 00:25:43,240 --> 00:25:46,400 Speaker 1: these different layers, and between each layer things would get 441 00:25:46,440 --> 00:25:49,240 Speaker 1: encrypted in a different way. So from layer one to 442 00:25:49,320 --> 00:25:51,639 Speaker 1: layer two it would get encrypted layer two to layer three, 443 00:25:51,640 --> 00:25:53,879 Speaker 1: it would get encrypted layer three to layer forward get 444 00:25:53,920 --> 00:25:58,439 Speaker 1: a different level of encryption, and and furthermore, each each layer, 445 00:25:59,040 --> 00:26:02,719 Speaker 1: each node in this connection only knows the node before 446 00:26:02,760 --> 00:26:04,879 Speaker 1: it and after it, which is key. It doesn't know 447 00:26:04,920 --> 00:26:08,439 Speaker 1: the entire chain exactly. So the idea of being that 448 00:26:08,680 --> 00:26:12,080 Speaker 1: this node, this series of nodes makes a circuit. That 449 00:26:12,200 --> 00:26:18,000 Speaker 1: circuit is connecting your computer running a tour browser to 450 00:26:18,160 --> 00:26:21,840 Speaker 1: whatever site or whatever information you were trying to retrieve. 451 00:26:22,880 --> 00:26:27,040 Speaker 1: But that circuit of nodes has very limited information in 452 00:26:27,160 --> 00:26:31,040 Speaker 1: any individual piece of the overall circuit. Right, So if 453 00:26:31,080 --> 00:26:35,680 Speaker 1: you identified that there's one node in this network, and 454 00:26:35,760 --> 00:26:39,080 Speaker 1: you see that information is coming from uh, the node 455 00:26:39,080 --> 00:26:41,760 Speaker 1: immediately preceding it, and it's going to the node following it, 456 00:26:42,080 --> 00:26:44,840 Speaker 1: you wouldn't be able to reconstruct the rest of the circuit. 457 00:26:44,920 --> 00:26:46,920 Speaker 1: That's all the information you would be able to get. 458 00:26:47,160 --> 00:26:50,000 Speaker 1: So if there are like six nodes in this circuit 459 00:26:50,400 --> 00:26:53,960 Speaker 1: and you've identified node number three, you can only see 460 00:26:54,000 --> 00:26:56,320 Speaker 1: that information is coming from node two and it's going 461 00:26:56,359 --> 00:26:58,120 Speaker 1: to node four. You wouldn't be able to see where 462 00:26:58,119 --> 00:27:01,520 Speaker 1: node one, five, or six, where those are in that circuit. Yeah, 463 00:27:01,560 --> 00:27:03,920 Speaker 1: you wouldn't be able to see the original sender or 464 00:27:03,960 --> 00:27:07,040 Speaker 1: the intended receiver, and hopefully if it's encrypted well enough, 465 00:27:07,080 --> 00:27:09,120 Speaker 1: you wouldn't be able to read the message either exactly, 466 00:27:09,160 --> 00:27:13,840 Speaker 1: because again it gets encrypted between each node in that circuit. Uh, 467 00:27:13,960 --> 00:27:17,520 Speaker 1: it sounds pretty secure, right, Yeah, it sounds it sounds 468 00:27:17,520 --> 00:27:21,439 Speaker 1: pretty cool. What could possibly go wrong? Well, as it 469 00:27:21,440 --> 00:27:26,000 Speaker 1: turns out, there are ways to try and figure out 470 00:27:26,040 --> 00:27:29,200 Speaker 1: who is trying to access what so So in this 471 00:27:29,280 --> 00:27:32,720 Speaker 1: world where you're looking at all these connections that get 472 00:27:32,800 --> 00:27:35,680 Speaker 1: hidden because it's traveling through all these nodes, you might 473 00:27:35,720 --> 00:27:39,200 Speaker 1: be able to see all the potential start points and 474 00:27:39,280 --> 00:27:41,879 Speaker 1: all the potential end points, but you don't really know 475 00:27:41,960 --> 00:27:47,800 Speaker 1: which people are trying to access which sites or which servers. However, 476 00:27:47,840 --> 00:27:49,959 Speaker 1: if you were to be able to analyze all the 477 00:27:50,000 --> 00:27:54,639 Speaker 1: traffic across the network and build enough of a statistical model, 478 00:27:55,200 --> 00:27:58,760 Speaker 1: you could start weeding people out and start looking at 479 00:27:58,760 --> 00:28:02,320 Speaker 1: the potential people going to the potential end points, play 480 00:28:02,400 --> 00:28:05,040 Speaker 1: the something like the target game. You could use big 481 00:28:05,119 --> 00:28:09,120 Speaker 1: data to uh analyze and then maybe even predict. Yeah. 482 00:28:09,320 --> 00:28:13,399 Speaker 1: So essentially what you're this is really oversimplifying it. But 483 00:28:13,480 --> 00:28:17,639 Speaker 1: essentially you might see that, uh that let's say person A, 484 00:28:18,000 --> 00:28:22,560 Speaker 1: the anonymous A is trying to access Silk Road, all right, 485 00:28:22,920 --> 00:28:28,920 Speaker 1: and so you see an anonymous person's a's connection light up. 486 00:28:29,240 --> 00:28:32,719 Speaker 1: It then goes across these nodes which mix everything else up, 487 00:28:32,760 --> 00:28:35,879 Speaker 1: and you are already looking at Silk Road, so you 488 00:28:36,160 --> 00:28:40,440 Speaker 1: are specifically you've already identified the potential target and the 489 00:28:40,480 --> 00:28:43,480 Speaker 1: potential destination. And then you see that the silk Road 490 00:28:43,480 --> 00:28:45,440 Speaker 1: one lights up in the amount of time you would 491 00:28:45,440 --> 00:28:48,920 Speaker 1: expect for this message to have to transfer across these modes. 492 00:28:49,640 --> 00:28:52,480 Speaker 1: Then you'd say, uh, this is a potential hit. And 493 00:28:52,480 --> 00:28:56,040 Speaker 1: then you continue to analyze traffic. This can actually help 494 00:28:56,200 --> 00:29:00,280 Speaker 1: d H and animonize I can't even say it. How 495 00:29:00,320 --> 00:29:08,560 Speaker 1: do we thank you anemone D anemone the network? So 496 00:29:08,600 --> 00:29:11,000 Speaker 1: but you know, it really is this is a potential 497 00:29:11,000 --> 00:29:14,760 Speaker 1: way where you can figure out at least which connection 498 00:29:14,880 --> 00:29:17,520 Speaker 1: was trying to connect to which server. Uh, And it 499 00:29:17,600 --> 00:29:21,280 Speaker 1: just it steps back from the actual circuit entirely. And 500 00:29:21,320 --> 00:29:24,440 Speaker 1: it may not be enough to move on a person 501 00:29:25,000 --> 00:29:27,520 Speaker 1: you know with full legal backing, but it might be 502 00:29:27,640 --> 00:29:29,920 Speaker 1: enough to convince you to really look into that person 503 00:29:29,960 --> 00:29:35,600 Speaker 1: more closely. So there's really no safe harbor for complete 504 00:29:35,600 --> 00:29:40,280 Speaker 1: anonymity on tour because if somebody wants to find you 505 00:29:40,840 --> 00:29:43,480 Speaker 1: or if they want to find find the needle in 506 00:29:43,480 --> 00:29:48,440 Speaker 1: the haystack, with enough diligence, they can well, I mean 507 00:29:48,480 --> 00:29:52,160 Speaker 1: it would it's at least possible for them to for 508 00:29:52,160 --> 00:29:55,720 Speaker 1: for someone really determined and with the right resources to 509 00:29:55,800 --> 00:29:59,040 Speaker 1: be able to start narrowing things down right, Uh, certainly, 510 00:29:59,200 --> 00:30:01,880 Speaker 1: And there there are few other problems with with tour. 511 00:30:01,960 --> 00:30:04,400 Speaker 1: I mean, it's an open source thing. That's part of 512 00:30:04,400 --> 00:30:07,240 Speaker 1: the way that the system actually protects itself and a 513 00:30:07,640 --> 00:30:12,120 Speaker 1: kind of anti logical. It might encounterintuitive, but it really 514 00:30:12,280 --> 00:30:15,160 Speaker 1: is because it means that anyone can can go in 515 00:30:15,320 --> 00:30:17,880 Speaker 1: and look at this. So if someone changes something or 516 00:30:17,920 --> 00:30:20,400 Speaker 1: someone puts in a change, this is a community that's 517 00:30:20,440 --> 00:30:25,120 Speaker 1: looking after the whole the whole product. So it's not 518 00:30:25,400 --> 00:30:28,960 Speaker 1: something that would be easy to slip in without anyone 519 00:30:29,080 --> 00:30:32,960 Speaker 1: taking notice of it. Also, its origins kind of raise 520 00:30:33,040 --> 00:30:38,400 Speaker 1: some eyebrows to Yes, the origin from naval research, right, Yeah, well, 521 00:30:38,440 --> 00:30:41,840 Speaker 1: I mean, as it turns out, Uh, there are reasons why, 522 00:30:42,440 --> 00:30:45,680 Speaker 1: say a military organization would want to be able to 523 00:30:46,400 --> 00:30:51,160 Speaker 1: send information uh secretly or perhaps access information in secret 524 00:30:51,600 --> 00:30:54,480 Speaker 1: and even within itself, yeah, even without itself, even even 525 00:30:54,560 --> 00:30:58,200 Speaker 1: apart from other organizations within that same government. Uh. When 526 00:30:58,240 --> 00:31:00,160 Speaker 1: we talk about the n s A, there are their 527 00:31:00,200 --> 00:31:05,640 Speaker 1: government organizations that are equally upset as they quite a 528 00:31:05,720 --> 00:31:07,680 Speaker 1: few that, like you know, you know, there are citizens 529 00:31:07,720 --> 00:31:10,280 Speaker 1: who are up and up up all about this. I mean, 530 00:31:10,320 --> 00:31:13,920 Speaker 1: they're very upset about it, as I think they should be. Um, 531 00:31:14,280 --> 00:31:18,960 Speaker 1: that's my own personal opinion. But there are government organizations 532 00:31:19,520 --> 00:31:23,280 Speaker 1: they're they're working for the same people who are equally upset. 533 00:31:23,400 --> 00:31:26,600 Speaker 1: Their wheels within wheels would be the X files line. 534 00:31:26,640 --> 00:31:29,320 Speaker 1: I mean, you've got those, You've got those great rivalries 535 00:31:29,360 --> 00:31:31,480 Speaker 1: between the CIA and n s A that date back 536 00:31:31,520 --> 00:31:36,440 Speaker 1: to the the beginning of both organizations. And recently, as 537 00:31:36,520 --> 00:31:41,080 Speaker 1: we're recording this, more and more information about what we 538 00:31:41,120 --> 00:31:45,400 Speaker 1: would call friendly fire surveillance has leaked people who had 539 00:31:46,280 --> 00:31:50,520 Speaker 1: not only the wherewithal, but the motivation to keep their 540 00:31:50,560 --> 00:31:55,640 Speaker 1: communications private or anonymous. Like congres members of Congress found that, um, 541 00:31:55,760 --> 00:31:58,160 Speaker 1: not only was the n s A, but the FBI 542 00:31:58,280 --> 00:32:03,760 Speaker 1: as well, uh mono touring their monitoring their day to 543 00:32:03,880 --> 00:32:08,880 Speaker 1: day emails and phone calls, whatnot. The thing that was 544 00:32:09,240 --> 00:32:12,040 Speaker 1: was really important to underline here is that it's not 545 00:32:12,160 --> 00:32:17,400 Speaker 1: inherently sinister to serve the web anonymously. And it's possible 546 00:32:17,520 --> 00:32:19,240 Speaker 1: to do it, as we said an earlier thing, but 547 00:32:19,320 --> 00:32:22,480 Speaker 1: it's not really plausible and uh not for a long 548 00:32:22,600 --> 00:32:26,479 Speaker 1: term solution. No, No, it's once off and we uh 549 00:32:26,600 --> 00:32:29,600 Speaker 1: we do tell you, guys, listeners in UH and Jonathan 550 00:32:29,680 --> 00:32:35,040 Speaker 1: Lawns show, we show you how theoretically you could make 551 00:32:35,120 --> 00:32:39,720 Speaker 1: yourself if not impossible to trace, very very inconvenient to 552 00:32:39,800 --> 00:32:44,520 Speaker 1: do so. Right, But it's basically like like burner phone, burner, 553 00:32:44,640 --> 00:32:49,520 Speaker 1: internet connection, burner face like to go, yeah, you gotta 554 00:32:49,560 --> 00:32:52,800 Speaker 1: pretty much be uh. You have to really limit what 555 00:32:53,000 --> 00:32:55,360 Speaker 1: it is you want to do, and you have to 556 00:32:55,560 --> 00:32:57,800 Speaker 1: very much limit the way you do it. So in 557 00:32:57,880 --> 00:33:01,120 Speaker 1: other words, it's not like you can just use that 558 00:33:01,360 --> 00:33:03,520 Speaker 1: methodology to do everything you would want to do on 559 00:33:03,600 --> 00:33:05,960 Speaker 1: the web, because there's some cool stuff that's on the 560 00:33:06,040 --> 00:33:08,200 Speaker 1: web that I love to do that there's just no 561 00:33:08,320 --> 00:33:12,120 Speaker 1: way to do anonymously, not not truly right, Like can 562 00:33:12,200 --> 00:33:16,880 Speaker 1: you really have a full Corgy watching experience if you 563 00:33:17,000 --> 00:33:20,600 Speaker 1: can't log in and comment? That also is a reference 564 00:33:20,720 --> 00:33:23,520 Speaker 1: to the Text Stuff episode. You'll learn way more about 565 00:33:23,600 --> 00:33:27,800 Speaker 1: Corgy obsessions in that show. I think it's enthusiasm. I 566 00:33:27,840 --> 00:33:30,640 Speaker 1: don't think we've crossed the line into obsession. Yet we're 567 00:33:30,720 --> 00:33:34,040 Speaker 1: just let me close out a couple of tabs. So 568 00:33:34,120 --> 00:33:36,680 Speaker 1: while Jonathan's closing out a couple of tabs, I do 569 00:33:36,920 --> 00:33:38,959 Speaker 1: just want to set you guys up for one more 570 00:33:39,040 --> 00:33:42,160 Speaker 1: big question. UM. If you if you like our show 571 00:33:42,200 --> 00:33:44,560 Speaker 1: stuff they want you to know listeners, then then you'll 572 00:33:44,600 --> 00:33:48,120 Speaker 1: love tech stuff because they have also been talking about 573 00:33:48,920 --> 00:33:53,280 Speaker 1: several different revelations, um, both both with security and the 574 00:33:53,400 --> 00:33:56,000 Speaker 1: nuts and bolts about how these kind of things work. 575 00:33:56,080 --> 00:33:59,160 Speaker 1: So we highly recommend their show. And I have to 576 00:33:59,240 --> 00:34:02,320 Speaker 1: ask you guys, since you're the ones with to know how, UM, 577 00:34:03,120 --> 00:34:05,880 Speaker 1: if you had to guess or speculate, do you think 578 00:34:05,960 --> 00:34:09,560 Speaker 1: that there would be more news forthcoming like the whole 579 00:34:09,680 --> 00:34:13,520 Speaker 1: Snowden disclosure thing where he said, you know, he kicked 580 00:34:13,560 --> 00:34:16,400 Speaker 1: down the door of the news organizations and said this 581 00:34:16,640 --> 00:34:21,560 Speaker 1: buying on everybody. Is there anything else that would happen, 582 00:34:21,680 --> 00:34:25,359 Speaker 1: because it seems like that's the big well, I mean, 583 00:34:25,480 --> 00:34:28,840 Speaker 1: we only know what we know, right there's there's you 584 00:34:29,120 --> 00:34:31,360 Speaker 1: can bet a couple of things. You can bet that 585 00:34:31,560 --> 00:34:36,840 Speaker 1: anything that has happened since Snowdon has left is largely 586 00:34:37,000 --> 00:34:39,799 Speaker 1: unknown to us because he was the source of the leak. 587 00:34:40,400 --> 00:34:43,960 Speaker 1: So anything that has been done to address that or 588 00:34:44,120 --> 00:34:47,560 Speaker 1: change things, evolve the technologies that's being used, or or 589 00:34:47,760 --> 00:34:49,959 Speaker 1: to find tune them in different ways, or even apply 590 00:34:50,080 --> 00:34:53,160 Speaker 1: them and even more broad applications, or to fine tune 591 00:34:53,200 --> 00:34:55,439 Speaker 1: the process by which they make sure that other people 592 00:34:55,480 --> 00:34:58,200 Speaker 1: don't link their information. Yeah, all of that is unknown 593 00:34:58,280 --> 00:35:00,239 Speaker 1: to us, so we can't really be sure what's going on. 594 00:35:00,480 --> 00:35:03,080 Speaker 1: What we do know is just based upon the information 595 00:35:03,120 --> 00:35:05,960 Speaker 1: that's been revealed so far. There already have been abuses 596 00:35:06,000 --> 00:35:08,000 Speaker 1: of the system. So that's the other thing to keep 597 00:35:08,040 --> 00:35:11,279 Speaker 1: in mind. Even if somehow you could agree that the 598 00:35:11,400 --> 00:35:16,120 Speaker 1: n s A system is on its own, maybe you 599 00:35:16,200 --> 00:35:18,600 Speaker 1: could call it flawed, but it mostly works. Let's say 600 00:35:18,640 --> 00:35:20,920 Speaker 1: that you even make that assumption. The problem is it's 601 00:35:20,960 --> 00:35:23,880 Speaker 1: run by people, and people, as it turns out, our 602 00:35:23,960 --> 00:35:28,120 Speaker 1: flawed very much so, and some people will take advantage 603 00:35:28,200 --> 00:35:32,200 Speaker 1: of having the opportunity to use such a powerful tool 604 00:35:32,320 --> 00:35:36,160 Speaker 1: to do things like snoop on X girlfriends. Yeah, and 605 00:35:36,400 --> 00:35:39,279 Speaker 1: even if someone isn't doing it nefariously, there there could 606 00:35:39,320 --> 00:35:42,480 Speaker 1: certainly be mistakes made. Yeah. So, so there are a 607 00:35:42,520 --> 00:35:45,279 Speaker 1: lot of issues that will probably come to light as 608 00:35:45,800 --> 00:35:50,880 Speaker 1: we get more people investigating this um. The interesting thing 609 00:35:50,960 --> 00:35:53,200 Speaker 1: to me is really seeing how much movement we see 610 00:35:53,200 --> 00:35:57,000 Speaker 1: in political circles to actually address this in a meaningful way, 611 00:35:57,680 --> 00:36:00,640 Speaker 1: because you do have lots of people, You have lots 612 00:36:00,680 --> 00:36:05,480 Speaker 1: of representatives who are at least saying that they want more, more, 613 00:36:05,719 --> 00:36:10,919 Speaker 1: more transparency because their constituents are demanding it. Right. Yeah, 614 00:36:10,920 --> 00:36:12,839 Speaker 1: well they're they're kind of demanding it too. I mean 615 00:36:13,200 --> 00:36:16,000 Speaker 1: it sounds like, yeah, once they found out that the 616 00:36:16,480 --> 00:36:20,960 Speaker 1: ad but uh there. That's one of the big debates 617 00:36:21,000 --> 00:36:25,160 Speaker 1: always is uh is it a matter of sincere offense 618 00:36:25,280 --> 00:36:30,680 Speaker 1: or fashionable offense fashionable indignation? And and that's something that 619 00:36:30,840 --> 00:36:35,920 Speaker 1: I think we will see in the future with our listeners. 620 00:36:36,280 --> 00:36:38,320 Speaker 1: We'd like to we'd like to hear from you guys 621 00:36:38,400 --> 00:36:43,600 Speaker 1: as well. What do you think the next big revelations 622 00:36:43,880 --> 00:36:48,120 Speaker 1: about the internet would be? Um? Oh, and here's one. Uh, 623 00:36:48,640 --> 00:36:52,279 Speaker 1: can you or have you served the web anonymously? Let's 624 00:36:52,280 --> 00:36:54,880 Speaker 1: see if you could write in and let us know 625 00:36:55,440 --> 00:36:58,600 Speaker 1: and still stay anonymous. I don't know, let's just see 626 00:36:58,640 --> 00:37:01,160 Speaker 1: if it works. Uh. In the meantime, I'd like to 627 00:37:01,239 --> 00:37:03,960 Speaker 1: thank Jonathan and Lauren you guys, thank you so much 628 00:37:04,000 --> 00:37:07,359 Speaker 1: for coming on our show. Um, I wish I knew 629 00:37:07,920 --> 00:37:11,320 Speaker 1: what had happened to Matt. We haven't really said it 630 00:37:11,400 --> 00:37:12,920 Speaker 1: on air, but you want to go ahead, and I 631 00:37:13,080 --> 00:37:15,960 Speaker 1: actually I can reveal at this point that Matt in 632 00:37:16,120 --> 00:37:21,239 Speaker 1: fact was buried under a pile of corky puppies and 633 00:37:21,560 --> 00:37:24,560 Speaker 1: he's he's all right, but he's penned and cannot move. 634 00:37:24,880 --> 00:37:28,160 Speaker 1: He's the happiest that he has ever been. He is stuck. 635 00:37:28,360 --> 00:37:32,600 Speaker 1: He has been saying that I cannot breathe and that's okay, uh, 636 00:37:32,680 --> 00:37:36,000 Speaker 1: in various languages. It's weird. He actually is really fluent, 637 00:37:36,120 --> 00:37:39,440 Speaker 1: but only in that one phrase. Yeah, he's really smart, 638 00:37:39,600 --> 00:37:43,440 Speaker 1: but it's strange that he only knows that phrase. So, um, 639 00:37:43,680 --> 00:37:46,560 Speaker 1: I guess maybe I'll go try to find him and 640 00:37:47,040 --> 00:37:49,239 Speaker 1: get him out because we still need him for the show. Yeah, 641 00:37:49,360 --> 00:37:52,759 Speaker 1: he's got some stuff he needs to edit to and uh. 642 00:37:53,400 --> 00:37:56,080 Speaker 1: And honestly, those puppies are starting to get tired and 643 00:37:56,160 --> 00:37:57,680 Speaker 1: he just keeps on picking up the ones that are 644 00:37:57,719 --> 00:37:59,680 Speaker 1: wearing a nap and putting him back on his stomach. 645 00:37:59,800 --> 00:38:03,160 Speaker 1: So so there's okay, I know the pile of puppies 646 00:38:03,200 --> 00:38:06,520 Speaker 1: you were talking about. Okay, No, he's under there, the 647 00:38:06,680 --> 00:38:13,359 Speaker 1: third pile. Yes, yeah, okay, great, um as I said, guys, 648 00:38:13,400 --> 00:38:15,520 Speaker 1: want to thank you so much for coming on the 649 00:38:15,560 --> 00:38:19,279 Speaker 1: show and being our very first guest. I'd also like 650 00:38:19,400 --> 00:38:23,120 Speaker 1: to let listeners know that if you like this show, 651 00:38:23,200 --> 00:38:25,600 Speaker 1: as you said, you'll enjoy tech stuff. But these folks 652 00:38:25,680 --> 00:38:29,320 Speaker 1: aren't just on tech stuff. They are on another excellent 653 00:38:29,440 --> 00:38:33,480 Speaker 1: podcast called Forward Thinking that which is also a video series, 654 00:38:33,600 --> 00:38:36,520 Speaker 1: and you can see all three of us they think 655 00:38:36,560 --> 00:38:40,320 Speaker 1: at various points, participating in everyday science shananigans on a 656 00:38:40,360 --> 00:38:42,920 Speaker 1: show called brain Stuff. You can actually see all three 657 00:38:43,000 --> 00:38:46,400 Speaker 1: of us in in the episode about about product placement. 658 00:38:46,600 --> 00:38:51,799 Speaker 1: Oh boy, that one I forgot about that. Yeah, well, 659 00:38:51,880 --> 00:38:54,040 Speaker 1: if you want to see why they're laughing at me, 660 00:38:54,160 --> 00:38:56,799 Speaker 1: you can check that one out to Uh. You can 661 00:38:56,920 --> 00:38:59,279 Speaker 1: find stuff they don't want you to know. Dot com 662 00:38:59,480 --> 00:39:04,360 Speaker 1: for video and every podcast we've ever made, and of 663 00:39:04,440 --> 00:39:06,600 Speaker 1: course we're all over the internet. You can drop us 664 00:39:06,640 --> 00:39:10,320 Speaker 1: a line with a suggestion or feedback on Twitter or Facebook. 665 00:39:10,360 --> 00:39:11,960 Speaker 1: That's where we put a lot of the stories that 666 00:39:12,040 --> 00:39:14,440 Speaker 1: don't make it into videos or podcasts. So do check 667 00:39:14,520 --> 00:39:17,080 Speaker 1: it out. And if you'd like to cut has the 668 00:39:17,239 --> 00:39:21,400 Speaker 1: social media rigormar rule entirely, just send us. Uh, just 669 00:39:21,480 --> 00:39:24,440 Speaker 1: send us an email at our address. We are conspiracy 670 00:39:24,520 --> 00:39:30,759 Speaker 1: at how stuff works dot com. For more on this 671 00:39:30,960 --> 00:39:35,440 Speaker 1: topic another unexplained phenomenon, visit test two dot com slash 672 00:39:35,640 --> 00:39:38,720 Speaker 1: conspiracy stuff. You can also get in touch on Twitter 673 00:39:38,920 --> 00:39:41,120 Speaker 1: at the handle at conspiracy stuff.