1 00:00:00,080 --> 00:00:04,760 Speaker 1: Thank you so much for tuning in to this classic episode. 2 00:00:05,320 --> 00:00:11,080 Speaker 1: Yes you we know who you are. Well, maybe we don't, 3 00:00:11,200 --> 00:00:13,840 Speaker 1: but someone does. Right. Oh man, you guys we made 4 00:00:13,840 --> 00:00:18,640 Speaker 1: this in twenty wow. Yeah, and we asked the question 5 00:00:18,680 --> 00:00:23,239 Speaker 1: way back then, can you be anonymous on the Internet? Yeah, 6 00:00:23,280 --> 00:00:27,240 Speaker 1: it's a question still being asked today. Uh and I 7 00:00:27,280 --> 00:00:29,080 Speaker 1: think we all kind of know the answer. But there's 8 00:00:29,080 --> 00:00:31,480 Speaker 1: a lot too And it's really interesting to hear this 9 00:00:31,600 --> 00:00:34,680 Speaker 1: take from this oldie but goody classic episode. Check it 10 00:00:34,680 --> 00:00:39,240 Speaker 1: out from UFOs two Ghosts and Government cover Ups. History 11 00:00:39,360 --> 00:00:42,760 Speaker 1: is writ with unexplained events. You can turn back now 12 00:00:43,000 --> 00:00:45,280 Speaker 1: or learn the stuff that don't want you to now. 13 00:00:51,520 --> 00:00:55,080 Speaker 1: All right, hello, welcome to the show. If that intro 14 00:00:55,200 --> 00:00:58,280 Speaker 1: by our super producer Noel was familiar, then that means 15 00:00:58,320 --> 00:01:00,920 Speaker 1: you are in the right place. Lady and gentlemen. This 16 00:01:01,040 --> 00:01:04,679 Speaker 1: is stuff they don't want you to know. I'm Ben. 17 00:01:04,800 --> 00:01:09,160 Speaker 1: You're probably wondering where Matt is. He is off on 18 00:01:09,319 --> 00:01:12,559 Speaker 1: an adventure that maybe we'll get to in the course 19 00:01:12,600 --> 00:01:15,880 Speaker 1: of this show. But in place of Matt today we 20 00:01:15,920 --> 00:01:21,399 Speaker 1: have not one but two very special guests from tech Stuff, 21 00:01:21,440 --> 00:01:24,920 Speaker 1: from brain Stuff, from Forward Thinking. I don't even know 22 00:01:24,920 --> 00:01:26,880 Speaker 1: how you guys found time to come on this show. 23 00:01:27,280 --> 00:01:30,640 Speaker 1: Jonathan Strickland and Lauren Vogel bomb Ben. We we found 24 00:01:30,640 --> 00:01:34,040 Speaker 1: time because we literally just recorded another tech stuff episode 25 00:01:34,040 --> 00:01:36,880 Speaker 1: with you, like like ten minutes ago. Thanks for not 26 00:01:36,880 --> 00:01:40,559 Speaker 1: giving away our time travel scheme. That was good. I'm 27 00:01:40,600 --> 00:01:44,000 Speaker 1: glad that we have got that out of the bag 28 00:01:44,040 --> 00:01:45,920 Speaker 1: out of the bag man, which is only gonna be 29 00:01:45,959 --> 00:01:48,840 Speaker 1: funny if you listen to the other episode, right, because 30 00:01:48,880 --> 00:01:53,560 Speaker 1: this is a sequel, that's a compacitorie, a sister episode. 31 00:01:53,840 --> 00:01:57,600 Speaker 1: It is it is now. You guys were kind enough 32 00:01:57,720 --> 00:01:59,680 Speaker 1: or add poor enough taste to have me on your 33 00:01:59,720 --> 00:02:03,200 Speaker 1: show just a few minutes ago when we recorded a 34 00:02:03,280 --> 00:02:08,120 Speaker 1: great episode on how to serve the Web anonymously or 35 00:02:08,200 --> 00:02:12,840 Speaker 1: whether it's even possible, And this was us to something 36 00:02:12,840 --> 00:02:16,120 Speaker 1: that we thought was right up the stuff they don't 37 00:02:16,120 --> 00:02:19,360 Speaker 1: want you to know, Ali because listeners out there now, 38 00:02:19,720 --> 00:02:23,560 Speaker 1: you guys know as well as everybody else does now 39 00:02:23,919 --> 00:02:27,320 Speaker 1: that it turns out the United States government in particular 40 00:02:27,840 --> 00:02:31,120 Speaker 1: was putting a lot more energy into tracking people than 41 00:02:31,160 --> 00:02:34,000 Speaker 1: we had all thought. Yeah, I mean, even if you 42 00:02:34,080 --> 00:02:36,960 Speaker 1: take it at face value, where you know you have 43 00:02:37,080 --> 00:02:39,720 Speaker 1: this this system, specifically the n s A, we're talking 44 00:02:39,720 --> 00:02:43,200 Speaker 1: about here with the end prison specifically with yes, but 45 00:02:43,639 --> 00:02:45,240 Speaker 1: if even if you take it at face value that 46 00:02:45,320 --> 00:02:48,359 Speaker 1: what they were looking for were foreign agents. So if 47 00:02:48,400 --> 00:02:53,079 Speaker 1: you are a United States citizen, then in theory you 48 00:02:53,120 --> 00:02:56,040 Speaker 1: would not a United States citizen who is not involved 49 00:02:56,040 --> 00:02:58,280 Speaker 1: in one of these schemes, you would not be considered 50 00:02:58,320 --> 00:03:01,880 Speaker 1: a foreign agent and therefore not part of the surveillance. 51 00:03:02,800 --> 00:03:06,280 Speaker 1: Even if you take that at face value, they would 52 00:03:06,600 --> 00:03:10,160 Speaker 1: look for people who they did identify as foreign agents, 53 00:03:10,200 --> 00:03:13,760 Speaker 1: and the people that that those folks talked to, right, Yeah, 54 00:03:13,800 --> 00:03:16,359 Speaker 1: they played the Kevin Bacon game. Yeah, And if you've 55 00:03:16,400 --> 00:03:19,639 Speaker 1: ever played the Kevin Bacon game, you know that it 56 00:03:20,320 --> 00:03:23,520 Speaker 1: gets pretty easy to start linking two people you never 57 00:03:23,520 --> 00:03:25,760 Speaker 1: thought would have had a connection to each other together. 58 00:03:26,600 --> 00:03:31,280 Speaker 1: It doesn't really take that many levels of separation, right, right. 59 00:03:31,360 --> 00:03:35,200 Speaker 1: And furthermore, they're really collecting information on everyone and sorting 60 00:03:35,240 --> 00:03:37,680 Speaker 1: through it in order to find these specific people. But 61 00:03:37,720 --> 00:03:40,640 Speaker 1: the fact that they're collecting and storing this information about 62 00:03:40,680 --> 00:03:45,480 Speaker 1: all of us, it's creepy, it's problematic. So again, even 63 00:03:45,520 --> 00:03:49,680 Speaker 1: taken on face value, it's it's it's troublesome. And then 64 00:03:49,680 --> 00:03:54,800 Speaker 1: when you realize the actual methodology, it becomes downright concerning. 65 00:03:54,800 --> 00:03:57,680 Speaker 1: I mean it's not just that, Oh, there's some issues here. 66 00:03:57,720 --> 00:04:03,320 Speaker 1: From a technological perspective, the very methodology is problematic. I 67 00:04:03,360 --> 00:04:06,480 Speaker 1: think that's well said. So one of my first questions 68 00:04:06,520 --> 00:04:10,600 Speaker 1: for you guys before you really get into this is, uh, if, 69 00:04:11,080 --> 00:04:14,320 Speaker 1: let's say twenty years ago, if someone had come up 70 00:04:14,360 --> 00:04:19,240 Speaker 1: to you in and said the government is watching everything 71 00:04:19,520 --> 00:04:21,680 Speaker 1: and it's just going to watch more, would you have 72 00:04:21,839 --> 00:04:24,480 Speaker 1: would you have thought it was sort of conspiracy bunk 73 00:04:24,680 --> 00:04:28,440 Speaker 1: or would you have thought there was a possibility of 74 00:04:28,480 --> 00:04:31,840 Speaker 1: it happening. I would have thought bunk for sure. And 75 00:04:32,600 --> 00:04:37,400 Speaker 1: it's and it's largely because uh, well, I mean the 76 00:04:37,480 --> 00:04:40,880 Speaker 1: capability just wasn't there any Yeah, when you're talking about 77 00:04:41,000 --> 00:04:46,359 Speaker 1: how much data gets created or transferred or copied or 78 00:04:46,440 --> 00:04:49,240 Speaker 1: transmitted however you want to look at it, it's an 79 00:04:49,240 --> 00:04:52,800 Speaker 1: astronomical number. It is an enormous number. Just let's just 80 00:04:52,880 --> 00:04:58,040 Speaker 1: take YouTube as the example of data creation. Okay, So 81 00:04:58,080 --> 00:05:01,680 Speaker 1: on YouTube, every single minute that passes more than a 82 00:05:01,760 --> 00:05:05,360 Speaker 1: hundred hours of video footage is being uploaded to YouTube. 83 00:05:05,720 --> 00:05:08,480 Speaker 1: Some of that's duplicate footage, but it doesn't really matter. 84 00:05:08,640 --> 00:05:12,000 Speaker 1: That's a huge amount of information. Now, just kind of extrapolate. 85 00:05:12,120 --> 00:05:16,280 Speaker 1: Imagine that the entire Internet, this network of computer networks, 86 00:05:17,040 --> 00:05:19,720 Speaker 1: is filled with people who are either creating information or 87 00:05:19,760 --> 00:05:23,240 Speaker 1: accessing it in some way, and the accessing of information 88 00:05:23,279 --> 00:05:27,760 Speaker 1: does in itself create a certain amount of meta data information. 89 00:05:27,839 --> 00:05:31,719 Speaker 1: There's information about who, who accessed what and when. This 90 00:05:31,800 --> 00:05:34,080 Speaker 1: is such an enormous amount that most of us would 91 00:05:34,120 --> 00:05:38,040 Speaker 1: think that being able to to capture it and filter 92 00:05:38,120 --> 00:05:40,440 Speaker 1: it and make any meaning of it would be a 93 00:05:40,480 --> 00:05:44,720 Speaker 1: Gardentian task beyond our capabilities. But it is a Gardentian task. 94 00:05:44,760 --> 00:05:47,520 Speaker 1: But it's not, as it turns out, beyond our capability exactly. 95 00:05:47,600 --> 00:05:51,680 Speaker 1: It turns out that our our technology is sufficiently sophisticated 96 00:05:51,760 --> 00:05:54,360 Speaker 1: enough to be able to weed through that kind of stuff, 97 00:05:54,520 --> 00:05:56,520 Speaker 1: or at least we're working very hard on it. I mean, 98 00:05:56,520 --> 00:05:58,599 Speaker 1: not the three of us at this table, but I'm 99 00:05:58,600 --> 00:06:02,719 Speaker 1: personally aware of and and Google is a perfect example 100 00:06:02,880 --> 00:06:07,240 Speaker 1: of how we should have really kind of re evaluated 101 00:06:07,320 --> 00:06:10,320 Speaker 1: this idea that it's just too big, right, because Google 102 00:06:10,640 --> 00:06:15,040 Speaker 1: has created a business that or at least the forward 103 00:06:15,160 --> 00:06:17,799 Speaker 1: facing part of their business that that's the search engine. 104 00:06:18,160 --> 00:06:20,440 Speaker 1: You know, arguably you would say that the real business 105 00:06:20,480 --> 00:06:23,120 Speaker 1: is advertising, But but the search engine is the thing, 106 00:06:23,240 --> 00:06:25,880 Speaker 1: the product that we're all familiar with, and it's really 107 00:06:25,880 --> 00:06:28,320 Speaker 1: really good. It's a really good search engine if you're 108 00:06:28,320 --> 00:06:32,360 Speaker 1: trying to find something specific. And once we realize how 109 00:06:32,440 --> 00:06:35,600 Speaker 1: much information is out there and how Google has developed 110 00:06:35,600 --> 00:06:39,120 Speaker 1: an algorithm that can effectively find the stuff you actually 111 00:06:39,120 --> 00:06:42,240 Speaker 1: want to see, then you start to realize, Oh, I 112 00:06:42,320 --> 00:06:45,320 Speaker 1: guess it really is possible to build in that a 113 00:06:45,440 --> 00:06:49,240 Speaker 1: similar kind of tool that could be useful if you're 114 00:06:49,279 --> 00:06:53,719 Speaker 1: looking for signs of activity. Uh, if you happen to 115 00:06:53,760 --> 00:06:57,280 Speaker 1: be an enormous government organization that is in charge of 116 00:06:57,440 --> 00:07:01,799 Speaker 1: discovering cryptic messages that between foreign agents that could potentially 117 00:07:01,839 --> 00:07:05,320 Speaker 1: affect your country, or friends of foreign agents, yes, or 118 00:07:05,440 --> 00:07:08,400 Speaker 1: people who know friends of foreign agents. Yes. That's the 119 00:07:08,400 --> 00:07:11,120 Speaker 1: problem is that this this ripples outward. Right if if 120 00:07:11,160 --> 00:07:14,560 Speaker 1: it were just the foreign agents, that becomes an issue 121 00:07:14,560 --> 00:07:17,080 Speaker 1: because then you have to be able to to reliably 122 00:07:17,200 --> 00:07:19,800 Speaker 1: identify a foreign agent versus someone who is not a 123 00:07:19,840 --> 00:07:22,880 Speaker 1: foreign agent. But then if you go one ripple outward, 124 00:07:22,920 --> 00:07:25,800 Speaker 1: who are these people talking to and why? Well, I 125 00:07:25,840 --> 00:07:27,800 Speaker 1: can understand why you would be interested in that, but 126 00:07:27,840 --> 00:07:30,440 Speaker 1: they're going to be people in that that one ripple 127 00:07:30,480 --> 00:07:33,960 Speaker 1: outward who are not in any way connected with anything nefarious, 128 00:07:33,960 --> 00:07:37,360 Speaker 1: but they're going to get swept up in that surveillance anyway. 129 00:07:37,480 --> 00:07:40,320 Speaker 1: If you go a further ring out, go for it. 130 00:07:40,520 --> 00:07:44,000 Speaker 1: That's an enormous number of people. This is what Facebook 131 00:07:44,040 --> 00:07:47,960 Speaker 1: is completely built upon, the whole idea of that social network. 132 00:07:48,640 --> 00:07:50,960 Speaker 1: If you ever see any of their presentations, you just 133 00:07:51,040 --> 00:07:54,960 Speaker 1: see how this, this this one small group of people 134 00:07:54,960 --> 00:07:57,960 Speaker 1: who have these inner connections between each other, become this 135 00:07:58,280 --> 00:08:01,280 Speaker 1: ginormous mass of people when you just go out a 136 00:08:01,280 --> 00:08:05,640 Speaker 1: couple of steps. Yet now this I'm glad that we're 137 00:08:05,640 --> 00:08:08,160 Speaker 1: talking about this because this is something that escapes a 138 00:08:08,160 --> 00:08:11,920 Speaker 1: lot of people that the uh, the government agency, the 139 00:08:12,040 --> 00:08:17,800 Speaker 1: n s A has automated the collection and analysis of 140 00:08:17,840 --> 00:08:20,560 Speaker 1: this of this stuff. For people who are concerned, you know, 141 00:08:20,600 --> 00:08:23,120 Speaker 1: the n s A is listening in on my phone 142 00:08:23,120 --> 00:08:26,440 Speaker 1: calls and reading my emails. They're taking your phone calls 143 00:08:26,600 --> 00:08:29,760 Speaker 1: and emails and they're keeping them like like you said, Lauren, 144 00:08:30,080 --> 00:08:32,160 Speaker 1: And what they'll do is if you pop up on 145 00:08:32,200 --> 00:08:35,320 Speaker 1: a different algorithm than they'll actually have a human Like 146 00:08:35,360 --> 00:08:39,280 Speaker 1: any other big organization, from a call center to the FBI, 147 00:08:39,720 --> 00:08:42,040 Speaker 1: it's kind of difficult to actually get to a human, 148 00:08:42,080 --> 00:08:44,280 Speaker 1: You have to go through a few steps. You gotta 149 00:08:44,320 --> 00:08:48,839 Speaker 1: dial one, wait for stuff, you listen to a robot voice. 150 00:08:48,920 --> 00:08:51,280 Speaker 1: And I hate it this side note. I hate it 151 00:08:51,320 --> 00:08:54,920 Speaker 1: when the the automated line won't let you just push 152 00:08:54,920 --> 00:08:57,360 Speaker 1: a button and you have to stand there wherever you 153 00:08:57,400 --> 00:09:01,360 Speaker 1: are and look really stupid and go operator operate or 154 00:09:01,400 --> 00:09:07,400 Speaker 1: you're saying yes, yes, yeah, and everybody is so solemn. 155 00:09:07,440 --> 00:09:11,040 Speaker 1: We all just said the same yes. But um. But 156 00:09:11,360 --> 00:09:14,760 Speaker 1: moving from this, we before we go too deep on 157 00:09:14,760 --> 00:09:17,520 Speaker 1: the government, we should make a point that they're not 158 00:09:17,600 --> 00:09:21,839 Speaker 1: the only people looking at web activity when we talk. Yeah, 159 00:09:21,920 --> 00:09:24,880 Speaker 1: I mean, companies obviously are looking at a lot of 160 00:09:24,920 --> 00:09:29,199 Speaker 1: web activity as well, for multiple reasons. Uh. Mostly companies 161 00:09:29,240 --> 00:09:31,880 Speaker 1: are looking at activity in order to make more money. Right, 162 00:09:31,880 --> 00:09:33,680 Speaker 1: they want to be able to sell you stuff better, 163 00:09:33,880 --> 00:09:37,040 Speaker 1: or to sell you as a consumer to an advertising 164 00:09:37,040 --> 00:09:40,720 Speaker 1: company so that that advertising company can sell you stuff better. Yeah. Yeah, 165 00:09:40,760 --> 00:09:43,320 Speaker 1: it all comes down to, you know, where are the dollars, 166 00:09:43,679 --> 00:09:45,520 Speaker 1: where are they coming from, and where are they going to? 167 00:09:45,679 --> 00:09:49,800 Speaker 1: And so we the users end up playing a big 168 00:09:49,880 --> 00:09:51,600 Speaker 1: role in that. I mean we we are the ones 169 00:09:51,640 --> 00:09:55,720 Speaker 1: who generate revenue for companies. And you know, if it 170 00:09:55,760 --> 00:09:58,200 Speaker 1: weren't that way, the Internet would be a very different place. 171 00:09:58,280 --> 00:10:00,720 Speaker 1: For one thing, it would not be near as robust 172 00:10:00,760 --> 00:10:03,080 Speaker 1: as it is, right it would it would be mainly 173 00:10:03,480 --> 00:10:07,880 Speaker 1: limited to communication lines between things like research institutes and 174 00:10:07,920 --> 00:10:11,560 Speaker 1: the government, which, big surprise, that's what originally the Internet 175 00:10:11,600 --> 00:10:16,440 Speaker 1: was all about. Yeah, ur ponnette was essentially connections between 176 00:10:16,679 --> 00:10:21,800 Speaker 1: scientific research institutes, universities, and government installations. And that was it, 177 00:10:22,320 --> 00:10:24,920 Speaker 1: and and that was because you know, that was what 178 00:10:25,040 --> 00:10:26,959 Speaker 1: it was built for. Was built to be this really 179 00:10:27,080 --> 00:10:31,440 Speaker 1: fast communications and networking ability so that people could share 180 00:10:31,520 --> 00:10:36,679 Speaker 1: things very effectively. What it's grown into is this crazy 181 00:10:36,800 --> 00:10:41,360 Speaker 1: world that melds things like entertainment and commerce and communications 182 00:10:41,760 --> 00:10:45,000 Speaker 1: all into one big package, which in many ways is 183 00:10:45,080 --> 00:10:48,600 Speaker 1: legitimately awesome. Obviously, we wouldn't have jobs if there were 184 00:10:48,760 --> 00:10:50,720 Speaker 1: an internet, or at least we wouldn't have these jobs 185 00:10:51,080 --> 00:10:54,560 Speaker 1: we are on the internet right now. Yeah, well, you 186 00:10:54,600 --> 00:10:59,040 Speaker 1: guys get paid. I'm here for community service. Your internship 187 00:10:59,080 --> 00:11:02,240 Speaker 1: has lasted longer than any other I've ever seen, that's true. Hey, 188 00:11:02,280 --> 00:11:08,880 Speaker 1: will you sign off for my hours? Before this? One 189 00:11:09,000 --> 00:11:12,440 Speaker 1: question though, that a lot of people will have is 190 00:11:13,440 --> 00:11:15,720 Speaker 1: the following. Just a little bit of background here, Um, 191 00:11:15,760 --> 00:11:22,480 Speaker 1: the question is is surfing anonymously legal? Uh. The background 192 00:11:22,559 --> 00:11:27,640 Speaker 1: here is that often the desire to surf anonymously is 193 00:11:27,760 --> 00:11:32,079 Speaker 1: depicted as one that is inherently sinister. That is the 194 00:11:32,160 --> 00:11:36,240 Speaker 1: same sort of uh perception that is given to things 195 00:11:36,400 --> 00:11:40,360 Speaker 1: like torrents now or peer to peer networks. Peer to 196 00:11:40,400 --> 00:11:45,080 Speaker 1: peer networks as they are by themselves, are not sinister 197 00:11:45,320 --> 00:11:48,160 Speaker 1: or shady or illegal. Right. Appear to peer network is 198 00:11:48,240 --> 00:11:51,520 Speaker 1: just a means of distribution of files. Right. But when 199 00:11:51,520 --> 00:11:54,280 Speaker 1: you start distributing files that you don't have the right 200 00:11:54,320 --> 00:11:58,000 Speaker 1: to distribute network, that's where the illegal activity comes, and 201 00:11:58,040 --> 00:12:01,559 Speaker 1: then you end up sort of casting this shadow across 202 00:12:01,600 --> 00:12:05,720 Speaker 1: the entire technology. So there are companies like music and 203 00:12:05,760 --> 00:12:08,800 Speaker 1: movie companies that just say peer to peer networks alone 204 00:12:09,000 --> 00:12:12,640 Speaker 1: are bad, because those are a lot of the ways 205 00:12:12,679 --> 00:12:15,960 Speaker 1: that that illegal file sharing got spread around, you know, 206 00:12:16,080 --> 00:12:18,240 Speaker 1: just a few years ago. Now it's not even that 207 00:12:18,440 --> 00:12:20,360 Speaker 1: big of a deal because you can get pretty much 208 00:12:20,400 --> 00:12:25,120 Speaker 1: everything in a billion different places. But uh, you know, 209 00:12:25,360 --> 00:12:28,160 Speaker 1: if you're talking about just trying to serve anonymously, it 210 00:12:28,280 --> 00:12:31,600 Speaker 1: all depends upon where you are in the world. In 211 00:12:31,600 --> 00:12:34,680 Speaker 1: the United States, it's not a big deal. There's no 212 00:12:35,000 --> 00:12:38,120 Speaker 1: law that you'd be breaking by trying to hide your 213 00:12:38,160 --> 00:12:41,120 Speaker 1: IP address. The laws that you would be breaking would 214 00:12:41,120 --> 00:12:43,840 Speaker 1: be if you try to do anything illegal while you 215 00:12:43,880 --> 00:12:46,120 Speaker 1: were doing that, or even if you weren't, whether or 216 00:12:46,120 --> 00:12:48,160 Speaker 1: not you're trying to hide what you're doing. If you're 217 00:12:48,160 --> 00:12:51,560 Speaker 1: doing something illegal, that's against the law, right, Yeah, Like 218 00:12:51,559 --> 00:12:53,520 Speaker 1: like you like you don't have to use tour to 219 00:12:53,600 --> 00:12:55,800 Speaker 1: buy drugs. There are many other reasons that you can 220 00:12:55,880 --> 00:12:58,160 Speaker 1: use tour, Yeah, but if you do buy drugs, that's 221 00:12:58,160 --> 00:13:01,000 Speaker 1: against the law. And then if you're caught, you could 222 00:13:01,000 --> 00:13:03,280 Speaker 1: be punished for it, or you will be if you're caught. 223 00:13:03,720 --> 00:13:06,360 Speaker 1: I see. Yeah. So so here in the West and 224 00:13:06,400 --> 00:13:09,760 Speaker 1: the United States, Canada and so on. Uh, there is 225 00:13:09,880 --> 00:13:14,240 Speaker 1: not a law against uh surfing the web anonymously. I don't. 226 00:13:14,280 --> 00:13:17,800 Speaker 1: I don't think it's even against the law to uh 227 00:13:18,000 --> 00:13:20,640 Speaker 1: go into some of the deep web stuff to which 228 00:13:20,679 --> 00:13:23,319 Speaker 1: we alluded, um, which you guys have covered in a 229 00:13:23,400 --> 00:13:25,520 Speaker 1: previous episode and Matt and I have covered as well. 230 00:13:25,920 --> 00:13:28,480 Speaker 1: We're talking about this Silk Road and and not the 231 00:13:28,559 --> 00:13:32,120 Speaker 1: historic one through Central Asia. But as you said, other 232 00:13:32,200 --> 00:13:35,680 Speaker 1: countries have different perspectives, Like you've got a great example 233 00:13:35,720 --> 00:13:39,600 Speaker 1: about China. Sure, Yeah, China. They have a program in 234 00:13:39,679 --> 00:13:43,800 Speaker 1: China that is called the Golden Shield Project, also more 235 00:13:43,840 --> 00:13:47,720 Speaker 1: commonly known as the Great Firewall of China UH. And 236 00:13:47,800 --> 00:13:50,040 Speaker 1: the reason for this is that it said effort i'll 237 00:13:50,160 --> 00:13:54,200 Speaker 1: um on part of the Chinese government to censor and 238 00:13:54,200 --> 00:13:57,880 Speaker 1: and have surveillance over Internet activities within China, so that 239 00:13:58,400 --> 00:14:01,600 Speaker 1: the main purpose of it is to perfect event objectionable 240 00:14:01,679 --> 00:14:05,560 Speaker 1: material as defined by the Chinese government from getting to 241 00:14:05,840 --> 00:14:09,840 Speaker 1: Chinese citizens using the internet. So flat out block some 242 00:14:09,960 --> 00:14:14,480 Speaker 1: websites and some search terms and like some yeah things 243 00:14:14,480 --> 00:14:18,080 Speaker 1: like Facebook. You can't access Facebook in China using if 244 00:14:18,120 --> 00:14:20,120 Speaker 1: you were just trying to connect to a Chinese I 245 00:14:20,320 --> 00:14:24,200 Speaker 1: s P and go through normal methods domain name server. 246 00:14:24,720 --> 00:14:26,800 Speaker 1: So if you were to if you were to just 247 00:14:26,960 --> 00:14:30,160 Speaker 1: use like plug and play, you're you're just trying to 248 00:14:30,200 --> 00:14:32,760 Speaker 1: use your browser to get to certain places. You would 249 00:14:32,760 --> 00:14:35,560 Speaker 1: find out that there's some websites you just cannot access 250 00:14:35,640 --> 00:14:39,400 Speaker 1: that way in China. In order to access those websites, 251 00:14:39,440 --> 00:14:42,200 Speaker 1: you have to circumvent the protections that have been put 252 00:14:42,200 --> 00:14:46,280 Speaker 1: in place. UH. In general, this is not seen as 253 00:14:46,360 --> 00:14:49,680 Speaker 1: a huge deal, right. It's it's it's it's frowned upon. 254 00:14:49,760 --> 00:14:52,520 Speaker 1: You're not supposed to do it. But as far as 255 00:14:52,560 --> 00:14:57,680 Speaker 1: I am aware, no one has been uh persecuted and 256 00:14:57,920 --> 00:15:02,520 Speaker 1: or prosecuted for trying to circumnavigate the firewall of China. However, 257 00:15:02,880 --> 00:15:06,360 Speaker 1: if you were to do something such as post messages 258 00:15:06,480 --> 00:15:11,440 Speaker 1: that are anti Chinese government to websites, then that is 259 00:15:11,600 --> 00:15:15,280 Speaker 1: very much considered against the law, and you will they 260 00:15:15,320 --> 00:15:17,320 Speaker 1: will look for you, and if they find you and 261 00:15:17,440 --> 00:15:20,360 Speaker 1: catch you, they'll punish you. Right, And it might also 262 00:15:20,440 --> 00:15:24,680 Speaker 1: be used as perhaps a pretext for arresting someone, at 263 00:15:24,720 --> 00:15:27,480 Speaker 1: least in that country, kind of the same way the 264 00:15:27,520 --> 00:15:31,880 Speaker 1: tax evasion was the crime for which Capone was ultimately arrested. Yeah. 265 00:15:31,960 --> 00:15:35,440 Speaker 1: That in some cases, depending again upon what country you 266 00:15:35,480 --> 00:15:39,080 Speaker 1: are in, uh, this might be the the door that 267 00:15:39,240 --> 00:15:40,960 Speaker 1: opens up so that they can get you for what 268 00:15:41,000 --> 00:15:43,880 Speaker 1: they really want you for. Right. And and when we 269 00:15:43,960 --> 00:15:48,000 Speaker 1: say we're not especially picking on China, oh, although I 270 00:15:48,040 --> 00:15:50,600 Speaker 1: do have to say, there's one really cool thing that 271 00:15:51,520 --> 00:15:53,720 Speaker 1: that freaks me out a bit. And if you are 272 00:15:54,240 --> 00:15:56,960 Speaker 1: on just the regular you know, the version of the 273 00:15:57,000 --> 00:16:01,120 Speaker 1: inner State uh Internet in China, every so often these 274 00:16:01,120 --> 00:16:04,640 Speaker 1: two cartoon police characters will pop up on the screen 275 00:16:05,120 --> 00:16:07,600 Speaker 1: just to let you know that they're they're looking out 276 00:16:07,640 --> 00:16:11,600 Speaker 1: for you. They're protecting you. They're protecting and serving by 277 00:16:11,640 --> 00:16:14,240 Speaker 1: making sure you're not doing anything wrong. Actually, what they're 278 00:16:14,280 --> 00:16:16,760 Speaker 1: doing is they're protecting you by making sure all that 279 00:16:16,880 --> 00:16:20,800 Speaker 1: terrible information that would flood flood your browser if if 280 00:16:20,800 --> 00:16:24,120 Speaker 1: it only had the chance, because they know you are 281 00:16:24,120 --> 00:16:27,280 Speaker 1: an upstanding Chinese sitizen and would never try to access 282 00:16:27,280 --> 00:16:29,240 Speaker 1: that kind of stuff. But that stuff is trying to 283 00:16:29,280 --> 00:16:31,720 Speaker 1: get it you no matter what, and those cops they 284 00:16:31,720 --> 00:16:33,520 Speaker 1: are making sure that you are going to be safe 285 00:16:33,520 --> 00:16:35,840 Speaker 1: from it. They also have blue eyes, which is very weird. 286 00:16:35,880 --> 00:16:38,280 Speaker 1: Their names are Chinging and cha Cha. It's based on 287 00:16:38,320 --> 00:16:42,560 Speaker 1: a pun that means police in Chinese. So check check 288 00:16:42,600 --> 00:16:44,400 Speaker 1: it out and google it if you have a chance. 289 00:16:44,520 --> 00:16:47,040 Speaker 1: Just remember that China will know you looked at it. 290 00:16:47,920 --> 00:16:53,800 Speaker 1: And uh so earlier, um, we we mentioned toward the 291 00:16:53,840 --> 00:16:57,760 Speaker 1: onion router right, and uh what I wanted to ask 292 00:16:57,800 --> 00:17:00,960 Speaker 1: about is if you could, because you guys are the 293 00:17:01,000 --> 00:17:05,120 Speaker 1: experts here on technical matters, if you could outline briefly 294 00:17:05,160 --> 00:17:09,920 Speaker 1: for our listeners, what's the difference between like a privacy 295 00:17:09,960 --> 00:17:13,560 Speaker 1: mode on a browser and something like tour That's a 296 00:17:13,600 --> 00:17:18,359 Speaker 1: great question. Yeah, in brief, a privacy browser on your 297 00:17:18,400 --> 00:17:22,600 Speaker 1: home computer does absolutely nothing to to protect what you 298 00:17:22,640 --> 00:17:25,200 Speaker 1: are doing from anyone aside from someone who is looking 299 00:17:25,440 --> 00:17:29,760 Speaker 1: purely at your home computer. Yeah, exactly, the text real quick, 300 00:17:30,520 --> 00:17:33,760 Speaker 1: while you're doing that, I'll continue to explain. So, yeah, 301 00:17:33,760 --> 00:17:36,640 Speaker 1: the privacy mode, what it's doing is it's preventing stuff 302 00:17:36,680 --> 00:17:39,680 Speaker 1: that would normally show up and say, your search history, 303 00:17:39,760 --> 00:17:42,920 Speaker 1: your browsing history cookies, is preventing all that kind of 304 00:17:42,920 --> 00:17:45,520 Speaker 1: stuff from happening, so that someone who gets access to 305 00:17:45,560 --> 00:17:48,000 Speaker 1: your machine can't just look and see what it is 306 00:17:48,000 --> 00:17:50,880 Speaker 1: you've been up to. However, anyone who can see the 307 00:17:50,920 --> 00:17:55,720 Speaker 1: traffic that's going across your local network that includes perhaps 308 00:17:55,720 --> 00:17:58,840 Speaker 1: other machines that are also on the local network, your router, 309 00:17:59,320 --> 00:18:03,520 Speaker 1: the modem, your I s P, all of these entities 310 00:18:03,680 --> 00:18:06,440 Speaker 1: know exactly what you're doing because in order for you 311 00:18:06,480 --> 00:18:10,080 Speaker 1: to get the stuff you're trying to get, they these 312 00:18:10,200 --> 00:18:13,160 Speaker 1: entities have to know where to send it, right, right, 313 00:18:13,200 --> 00:18:14,720 Speaker 1: This is so you know, up to and including the 314 00:18:14,760 --> 00:18:17,800 Speaker 1: website that you're accessing, they know who you are as well, right, Yeah, 315 00:18:17,840 --> 00:18:20,119 Speaker 1: at least they know the I P EDG yes, and 316 00:18:20,160 --> 00:18:22,720 Speaker 1: they know they know what network it's going to. So 317 00:18:22,880 --> 00:18:25,600 Speaker 1: really it's it's you know, you can't hide your IP 318 00:18:25,760 --> 00:18:29,960 Speaker 1: address perfectly because if you did, no information would ever 319 00:18:30,040 --> 00:18:33,199 Speaker 1: come back to your computer. Now we, uh, we do 320 00:18:33,359 --> 00:18:38,400 Speaker 1: have an interesting fact here, and by interesting I mean disturbing. 321 00:18:38,480 --> 00:18:41,720 Speaker 1: So I'll just go ahead and ask how much information 322 00:18:41,800 --> 00:18:45,000 Speaker 1: does someone a company or a government or whomever need 323 00:18:45,080 --> 00:18:47,160 Speaker 1: about you before they can figure out who you are? 324 00:18:47,480 --> 00:18:51,359 Speaker 1: All right? This is a kind of fascinating. Did you 325 00:18:51,400 --> 00:18:55,960 Speaker 1: ever hear the story about how Target had identified a 326 00:18:56,000 --> 00:19:00,040 Speaker 1: customer as being pregnant? It was it turned out to 327 00:19:00,080 --> 00:19:04,600 Speaker 1: be a young lady, teenager, and so Target pregnant people 328 00:19:04,600 --> 00:19:08,399 Speaker 1: are ladies, that that would be true. That is true, Lauren, 329 00:19:08,440 --> 00:19:13,040 Speaker 1: thank you? Is a good point. A young lady pregnant, 330 00:19:13,560 --> 00:19:17,280 Speaker 1: uh don't know. Parents don't know, parents don't know, she 331 00:19:17,320 --> 00:19:21,840 Speaker 1: has not told them, and Target starts proactively sending her 332 00:19:21,960 --> 00:19:26,080 Speaker 1: offers for things that a pregnant lady wouldn't possibly need. 333 00:19:26,880 --> 00:19:31,520 Speaker 1: And her father found the offers and got very upset, 334 00:19:31,600 --> 00:19:35,639 Speaker 1: saying like, why is Target sending this unsolit solicited stuff? 335 00:19:35,680 --> 00:19:37,720 Speaker 1: What do you what are you saying about? My daughter 336 00:19:38,320 --> 00:19:41,480 Speaker 1: raised a big fuss about it. You have offended mod dignity. 337 00:19:41,680 --> 00:19:43,920 Speaker 1: That's how I picture him speaking. Yes, it was a 338 00:19:43,960 --> 00:19:48,800 Speaker 1: Southern gentleman from the fun city of Savannah there with 339 00:19:48,800 --> 00:19:51,160 Speaker 1: a white glove and just slapped the front door. Yeah 340 00:19:51,400 --> 00:19:54,399 Speaker 1: challenge you, I challenge you on tie organization to a duel. 341 00:19:54,960 --> 00:19:57,159 Speaker 1: Yeah no, that's not exactly what happened, but he did 342 00:19:57,280 --> 00:20:00,760 Speaker 1: raise a fuss and then later wrote a second a 343 00:20:00,800 --> 00:20:04,320 Speaker 1: follow up message saying I had to talk with my daughter. 344 00:20:04,480 --> 00:20:06,159 Speaker 1: It turns out that I did not. I was not 345 00:20:06,280 --> 00:20:10,000 Speaker 1: aware that she was pregnant. But this raised the point 346 00:20:10,080 --> 00:20:13,159 Speaker 1: of how did Target know? What was it that gave 347 00:20:13,600 --> 00:20:16,919 Speaker 1: Target the information? How did they predict this? And as 348 00:20:16,960 --> 00:20:19,520 Speaker 1: it turned out, it had the company had been watching 349 00:20:19,520 --> 00:20:24,520 Speaker 1: her purchase patterns and determined that statistically speaking, she was 350 00:20:24,640 --> 00:20:28,760 Speaker 1: very likely pregnant. And so this is an illustration that 351 00:20:29,640 --> 00:20:33,119 Speaker 1: you don't have to have actively shared some information about 352 00:20:33,119 --> 00:20:37,919 Speaker 1: yourself for an entity or a person to draw conclusions 353 00:20:38,000 --> 00:20:42,840 Speaker 1: about at least your what your physical state is, or 354 00:20:42,880 --> 00:20:45,879 Speaker 1: what your your state of mind might be. Even if 355 00:20:45,920 --> 00:20:49,399 Speaker 1: it's not your specific name and identity, it could be 356 00:20:49,520 --> 00:20:52,200 Speaker 1: enough to be able to single out who you are 357 00:20:52,760 --> 00:20:56,080 Speaker 1: from a level that's separate from my name is Jonathan 358 00:20:56,080 --> 00:21:00,840 Speaker 1: Strickland and I live in Atlanta, right That actually would 359 00:21:00,880 --> 00:21:05,280 Speaker 1: be very easy. Then they're probably very few Strickland's living 360 00:21:05,280 --> 00:21:07,080 Speaker 1: in Atlanta. There might be a few, because you know, 361 00:21:07,119 --> 00:21:09,680 Speaker 1: there are a lot of other Johnathan Stricklands. Lauren vocal 362 00:21:09,680 --> 00:21:13,760 Speaker 1: Bam might be the easiest to paying directly to zoom 363 00:21:13,760 --> 00:21:19,359 Speaker 1: in pretty quickly. So the real answer to this question, 364 00:21:19,359 --> 00:21:23,800 Speaker 1: according to research specialists, is that thirty three bits of 365 00:21:23,920 --> 00:21:29,280 Speaker 1: information called bits of entropy and this in this identification business, 366 00:21:29,320 --> 00:21:31,719 Speaker 1: are required in order to narrow it down to a 367 00:21:31,760 --> 00:21:36,600 Speaker 1: specific person out of all the people on earth. And 368 00:21:36,600 --> 00:21:39,360 Speaker 1: and these these bits of information can be anything from 369 00:21:39,400 --> 00:21:42,040 Speaker 1: from your gender to the type of car you drive, 370 00:21:42,400 --> 00:21:45,679 Speaker 1: to your zip code to like like, it doesn't have 371 00:21:45,720 --> 00:21:48,520 Speaker 1: to be the same thirty three bits in order it 372 00:21:48,560 --> 00:21:51,120 Speaker 1: could be any thirty three bits, and bit in this 373 00:21:51,160 --> 00:21:55,359 Speaker 1: case means something specific, like like in the computer world 374 00:21:55,440 --> 00:21:59,000 Speaker 1: we talk about digital Uh, you know these binary digits. 375 00:21:59,080 --> 00:22:01,320 Speaker 1: That that's what it is. It's either a zero or 376 00:22:01,359 --> 00:22:03,399 Speaker 1: a one, which you could think of as either a 377 00:22:03,440 --> 00:22:08,600 Speaker 1: no or yes. Well, uh, some bits, some pieces of 378 00:22:08,640 --> 00:22:12,840 Speaker 1: information represent a single bit, like gender is considered to 379 00:22:12,840 --> 00:22:17,000 Speaker 1: be a single bit, putting gender discussions aside. For for 380 00:22:17,160 --> 00:22:20,119 Speaker 1: many people, this would be male or female, all right, 381 00:22:20,320 --> 00:22:24,200 Speaker 1: That that obviously oversimplifies things, but for the purposes purposes 382 00:22:24,200 --> 00:22:28,480 Speaker 1: for identification male female tends to be uh. The way 383 00:22:28,520 --> 00:22:30,400 Speaker 1: that they look at it, very black and white kind 384 00:22:30,400 --> 00:22:34,480 Speaker 1: of approach that represents one bit. Something like your zip 385 00:22:34,480 --> 00:22:39,360 Speaker 1: code might be several bits of information that would make 386 00:22:39,440 --> 00:22:42,320 Speaker 1: up just one zip code, but all it takes is 387 00:22:42,359 --> 00:22:45,240 Speaker 1: thirty three bits. Some of those bits might be connected 388 00:22:45,280 --> 00:22:49,560 Speaker 1: to a larger concept, like the your model of car, uh, 389 00:22:49,720 --> 00:22:53,040 Speaker 1: the specific region you live in, whatever it is, your age, 390 00:22:53,080 --> 00:22:55,919 Speaker 1: that's another good one. But all you need are thirty 391 00:22:56,000 --> 00:22:59,240 Speaker 1: three bits worth of this information to be able to 392 00:22:59,359 --> 00:23:02,000 Speaker 1: identify in the reason for that is you take this 393 00:23:02,200 --> 00:23:04,960 Speaker 1: yes or no. That's a base of two, right, you've 394 00:23:05,000 --> 00:23:08,119 Speaker 1: got two options. You take that too, Then you have 395 00:23:08,240 --> 00:23:11,280 Speaker 1: the thirty three different bits. That's two to the power 396 00:23:11,280 --> 00:23:13,920 Speaker 1: of thirty three. If you work that out, that ends 397 00:23:13,960 --> 00:23:17,280 Speaker 1: up being more than eight billion. Two to the thirty 398 00:23:17,320 --> 00:23:20,120 Speaker 1: third powers more than eight billion. There are seven billion 399 00:23:20,200 --> 00:23:23,800 Speaker 1: people on Earth. Wait, we've got made up people in 400 00:23:23,800 --> 00:23:27,280 Speaker 1: this It means that we have more than enough information 401 00:23:27,359 --> 00:23:29,959 Speaker 1: to to account for the seven billion people who are 402 00:23:30,000 --> 00:23:33,280 Speaker 1: actually alive. So uh, if you the idea is that 403 00:23:33,320 --> 00:23:35,760 Speaker 1: with those thirty three bits, you can then have enough 404 00:23:35,880 --> 00:23:40,000 Speaker 1: personal identifiable information to narrow it down to a specific individual. 405 00:23:40,280 --> 00:23:47,360 Speaker 1: And also it's devilishly easy to forget that what you're 406 00:23:47,400 --> 00:23:51,480 Speaker 1: putting out on the Internet personally identifies you, right say, 407 00:23:51,520 --> 00:23:54,640 Speaker 1: all sorts of things that they imagine are innocuous. I mean, 408 00:23:55,200 --> 00:23:59,320 Speaker 1: Twitter is in the Congressional Library. Now, yeah, you can 409 00:23:59,359 --> 00:24:02,520 Speaker 1: get an entire or you can download an entire Twitter history, 410 00:24:02,920 --> 00:24:05,600 Speaker 1: which is for some of us quite a large file. 411 00:24:06,000 --> 00:24:07,640 Speaker 1: It turns out I think I have more than seventeen 412 00:24:07,640 --> 00:24:12,080 Speaker 1: dozen tweets. So um, I clearly am not as as 413 00:24:12,280 --> 00:24:17,040 Speaker 1: worried about anonymity as some people are. Perhaps that is 414 00:24:17,240 --> 00:24:19,919 Speaker 1: a foolish thing on my part, but uh, there's an 415 00:24:19,960 --> 00:24:23,520 Speaker 1: interesting example of this as well. Researchers at Stanford and 416 00:24:23,560 --> 00:24:27,800 Speaker 1: the University of Texas. We're able to identify Netflix viewers 417 00:24:28,400 --> 00:24:30,960 Speaker 1: based upon their activity, and part of that was because 418 00:24:31,000 --> 00:24:33,679 Speaker 1: these are the same These viewers would do things like 419 00:24:33,800 --> 00:24:37,480 Speaker 1: leave reviews for movies on other sites, and just by 420 00:24:37,600 --> 00:24:40,199 Speaker 1: looking at this stuff that you wouldn't think would personally 421 00:24:40,240 --> 00:24:43,560 Speaker 1: identify you, right because it's just it's just you your 422 00:24:43,600 --> 00:24:48,119 Speaker 1: opinion about a movie. It's not hey, I happen to 423 00:24:48,280 --> 00:24:52,199 Speaker 1: be five foot whatever. I'm not telling you, yeah, but 424 00:24:52,280 --> 00:24:55,720 Speaker 1: they But the point being then is that there's uh there, 425 00:24:56,200 --> 00:25:00,000 Speaker 1: there's some puzzle solving that can happen very easily, really, 426 00:25:00,119 --> 00:25:06,240 Speaker 1: because they say, oh, um, Anonymous user A watch this 427 00:25:06,320 --> 00:25:09,639 Speaker 1: thing on Netflix at this time, and then oh surprise, 428 00:25:09,720 --> 00:25:14,639 Speaker 1: shortly thereafter Anonymous user oh wait, it's Anonymous user A. 429 00:25:14,880 --> 00:25:17,600 Speaker 1: And they said that this was they gave it three stars, 430 00:25:17,640 --> 00:25:19,800 Speaker 1: and what they did on Netflix like like this, this 431 00:25:19,920 --> 00:25:25,320 Speaker 1: Anonymous user A watched a particular movie at a particular time. 432 00:25:25,760 --> 00:25:28,800 Speaker 1: This other person whose identity we know left a review 433 00:25:28,840 --> 00:25:32,879 Speaker 1: on IMDb, and based upon the time between these two events, 434 00:25:32,920 --> 00:25:35,920 Speaker 1: were reasonably certain that Anonymous user A is this person 435 00:25:35,960 --> 00:25:39,760 Speaker 1: we know. And Anonymous User A is completely wrong about 436 00:25:39,760 --> 00:25:42,719 Speaker 1: Big Trouble and Little Chin, which is an amazing movie, 437 00:25:43,840 --> 00:25:46,159 Speaker 1: is not It is not a good bad movie. It 438 00:25:46,280 --> 00:25:48,879 Speaker 1: is a good good movie because he's this sidekick the 439 00:25:48,920 --> 00:25:53,720 Speaker 1: whole time. He got up express. Come on, So what 440 00:25:53,960 --> 00:25:56,800 Speaker 1: is the tour project about? You guys have done a 441 00:25:57,160 --> 00:25:59,760 Speaker 1: you guys done a podcast on this. Um Matt and 442 00:26:00,000 --> 00:26:02,000 Speaker 1: I have done some videos, but we've never done a 443 00:26:02,000 --> 00:26:05,960 Speaker 1: full podcast on it. So it's tour, you know, kind 444 00:26:05,960 --> 00:26:08,360 Speaker 1: of stands for the Onion router, it's really its own name. 445 00:26:08,359 --> 00:26:11,520 Speaker 1: Now it's just tour sure. Originally the Onion router was 446 00:26:11,600 --> 00:26:15,280 Speaker 1: based on the idea that, um, it's encrypting things in layers, yes, 447 00:26:15,560 --> 00:26:18,520 Speaker 1: so that you would go uh an information from point 448 00:26:18,520 --> 00:26:21,440 Speaker 1: A to point z, let's say, would go through all 449 00:26:21,480 --> 00:26:24,560 Speaker 1: these different layers, and between each layer things would get 450 00:26:24,680 --> 00:26:27,439 Speaker 1: encrypted in a different way. So from layer one to 451 00:26:27,520 --> 00:26:29,680 Speaker 1: layer two it would get encrypted, layer two to layer 452 00:26:29,720 --> 00:26:31,760 Speaker 1: three it would get encrypted. Layer three to layer for 453 00:26:32,000 --> 00:26:35,600 Speaker 1: get a different level of encryption. And and furthermore, each 454 00:26:35,760 --> 00:26:40,199 Speaker 1: each layer, each node in this connection only knows the 455 00:26:40,240 --> 00:26:42,680 Speaker 1: node before it and after it, which is key. It 456 00:26:42,720 --> 00:26:46,000 Speaker 1: doesn't know the entire chain exactly. So the idea of 457 00:26:46,000 --> 00:26:49,840 Speaker 1: being that this node, this series of nodes makes a circuit. 458 00:26:50,240 --> 00:26:55,520 Speaker 1: That circuit is connecting your computer running a tour browser 459 00:26:56,160 --> 00:26:59,959 Speaker 1: to whatever site or whatever information you were trying to retreat. 460 00:27:01,080 --> 00:27:05,280 Speaker 1: But that circuit of nodes has very limited information in 461 00:27:05,359 --> 00:27:09,280 Speaker 1: any individual piece of the overall circuit. Right, So if 462 00:27:09,320 --> 00:27:13,879 Speaker 1: you identified that there's one node in this network, and 463 00:27:14,000 --> 00:27:17,280 Speaker 1: you see that information is coming from uh the node 464 00:27:17,320 --> 00:27:19,959 Speaker 1: immediately preceding it, and it's going to the node following it, 465 00:27:20,280 --> 00:27:23,040 Speaker 1: you wouldn't be able to reconstruct the rest of the circuit. 466 00:27:23,119 --> 00:27:25,160 Speaker 1: That's all the information you would be able to get. 467 00:27:25,359 --> 00:27:28,199 Speaker 1: So if there are like six nodes in this circuit 468 00:27:28,600 --> 00:27:32,200 Speaker 1: and you've identified node number three, you can only see 469 00:27:32,240 --> 00:27:34,520 Speaker 1: that information is coming from node two and it's going 470 00:27:34,560 --> 00:27:36,320 Speaker 1: to node four. You wouldn't be able to see where 471 00:27:36,359 --> 00:27:39,760 Speaker 1: node one, five, or six, where those are in that circuit. Yeah, 472 00:27:39,760 --> 00:27:42,120 Speaker 1: you wouldn't be able to see the original sender or 473 00:27:42,160 --> 00:27:45,280 Speaker 1: the intended receiver. And hopefully if it's encrypted well enough, 474 00:27:45,320 --> 00:27:47,360 Speaker 1: you wouldn't be able to read the message either exactly, 475 00:27:47,400 --> 00:27:52,040 Speaker 1: because again it gets encrypted between each node in that circuit. Uh, 476 00:27:52,160 --> 00:27:55,720 Speaker 1: it sounds pretty secure, right, Yeah, it sounds it sounds 477 00:27:55,760 --> 00:27:59,639 Speaker 1: pretty cool. What could possibly go wrong? Well, as it 478 00:27:59,640 --> 00:28:04,240 Speaker 1: turns out, there are ways to try and figure out 479 00:28:04,280 --> 00:28:07,440 Speaker 1: who is trying to access what so so in this 480 00:28:07,520 --> 00:28:10,920 Speaker 1: world where you're looking at all these connections that get 481 00:28:11,040 --> 00:28:13,920 Speaker 1: hidden because it's traveling through all these nodes, you might 482 00:28:13,960 --> 00:28:17,440 Speaker 1: be able to see all the potential start points and 483 00:28:17,480 --> 00:28:20,119 Speaker 1: all the potential end points, but you don't really know 484 00:28:20,200 --> 00:28:26,040 Speaker 1: which people are trying to access which sites or which servers. However, 485 00:28:26,080 --> 00:28:28,199 Speaker 1: if you were to be able to analyze all the 486 00:28:28,240 --> 00:28:32,440 Speaker 1: traffic across the network and build enough of a statistical 487 00:28:32,520 --> 00:28:36,680 Speaker 1: model you could start weeding people out and start looking 488 00:28:36,800 --> 00:28:39,840 Speaker 1: at the potential people going to the potential end points. 489 00:28:40,120 --> 00:28:42,600 Speaker 1: You could play the something like the target game. You 490 00:28:42,640 --> 00:28:46,320 Speaker 1: could use big data to uh analyze and then maybe 491 00:28:46,320 --> 00:28:50,320 Speaker 1: even predict. Yeah, so essentially what you're this is really 492 00:28:50,360 --> 00:28:54,520 Speaker 1: oversimplifying it. But essentially you might see that, uh that, 493 00:28:54,640 --> 00:28:58,480 Speaker 1: let's say person A, the anonymous A is trying to 494 00:28:58,560 --> 00:29:04,040 Speaker 1: access silk row, all right, and so you see an 495 00:29:04,480 --> 00:29:08,320 Speaker 1: anonymous person's a's connection light up. It then goes across 496 00:29:08,360 --> 00:29:11,680 Speaker 1: these nodes which mix everything else up, and you are 497 00:29:11,760 --> 00:29:15,480 Speaker 1: already looking at silk Road, so you are specifically you've 498 00:29:15,520 --> 00:29:20,360 Speaker 1: already identified the potential target and the potential destination. And 499 00:29:20,400 --> 00:29:22,480 Speaker 1: then you see that the silk Road one lights up 500 00:29:22,520 --> 00:29:24,440 Speaker 1: in the amount of time you would expect for this 501 00:29:24,520 --> 00:29:29,400 Speaker 1: message to have to transfer across these modes. Then you'd say, uhh, 502 00:29:29,520 --> 00:29:31,560 Speaker 1: this is a potential hit. And then you continue to 503 00:29:31,600 --> 00:29:37,280 Speaker 1: analyze traffic. This can actually help duh and anemonize. I 504 00:29:37,280 --> 00:29:40,600 Speaker 1: can't even say it, and how do we thank you? 505 00:29:41,280 --> 00:29:47,320 Speaker 1: Anemone d anemone the network. So but you know, it 506 00:29:47,400 --> 00:29:50,000 Speaker 1: really is this is a potential way where you can 507 00:29:50,280 --> 00:29:53,959 Speaker 1: figure out at least which connection was trying to connect 508 00:29:53,960 --> 00:29:57,080 Speaker 1: to which server. Uh. And it just it steps back 509 00:29:57,160 --> 00:30:00,160 Speaker 1: from the actual circuit entirely. And it may not be 510 00:30:00,400 --> 00:30:04,200 Speaker 1: enough to move on a person you know with full 511 00:30:04,320 --> 00:30:06,840 Speaker 1: legal backing, but it might be enough to convince you 512 00:30:06,880 --> 00:30:09,880 Speaker 1: to really look into that person more closely. So there's 513 00:30:10,120 --> 00:30:16,400 Speaker 1: really no safe harbor for complete anonymity on tour because 514 00:30:16,440 --> 00:30:19,640 Speaker 1: if somebody wants to find you, or if they want 515 00:30:19,680 --> 00:30:24,240 Speaker 1: to find find the needle in the haystack, with enough diligence, 516 00:30:24,280 --> 00:30:28,040 Speaker 1: they can well, I mean it would it's at least 517 00:30:28,160 --> 00:30:32,240 Speaker 1: possible for them to for for someone really determined and 518 00:30:32,320 --> 00:30:35,080 Speaker 1: with the right resources to be able to start narrowing 519 00:30:35,080 --> 00:30:38,160 Speaker 1: things down right. Uh, certainly. And there there are a 520 00:30:38,240 --> 00:30:41,080 Speaker 1: few other problems with with tour. I mean, it's an 521 00:30:41,120 --> 00:30:43,040 Speaker 1: open source thing. That's part of the way that this 522 00:30:43,160 --> 00:30:46,720 Speaker 1: is tom actually protects itself and a kind of anti 523 00:30:46,800 --> 00:30:51,720 Speaker 1: logical encounterintuitive, but it really is because it means that 524 00:30:51,800 --> 00:30:54,280 Speaker 1: anyone can can go in and look at this. So 525 00:30:54,320 --> 00:30:57,240 Speaker 1: if someone changes something or someone puts in a change, 526 00:30:57,280 --> 00:31:01,360 Speaker 1: this is a community that's looking after the whole the 527 00:31:01,400 --> 00:31:04,600 Speaker 1: whole product, so it's not something that would be easy 528 00:31:04,680 --> 00:31:09,680 Speaker 1: to slip in without anyone taking notice of it. Also, 529 00:31:09,840 --> 00:31:13,280 Speaker 1: its origins kind of raise some eyebrows to yes, the 530 00:31:13,320 --> 00:31:17,080 Speaker 1: origin from naval research, right, Yeah, well, I mean, as 531 00:31:17,080 --> 00:31:21,200 Speaker 1: it turns out, uh, there are reasons why, say a 532 00:31:21,320 --> 00:31:25,720 Speaker 1: military organization would want to be able to send information 533 00:31:26,040 --> 00:31:31,400 Speaker 1: uh secretly or perhaps access information in secret and itself, yeah, 534 00:31:31,440 --> 00:31:34,560 Speaker 1: even without itself, even even apart from other organizations within 535 00:31:34,600 --> 00:31:37,360 Speaker 1: that same government. Uh, when we talk about the n 536 00:31:37,440 --> 00:31:40,280 Speaker 1: s A, there are other government organizations that are equally 537 00:31:40,400 --> 00:31:44,920 Speaker 1: upset as yea, quite a few that, like you know, 538 00:31:44,960 --> 00:31:47,120 Speaker 1: you know, there are citizens who are up in, up, 539 00:31:47,240 --> 00:31:49,719 Speaker 1: up all about this. I mean they're very upset about it, 540 00:31:49,840 --> 00:31:53,080 Speaker 1: as I think they should be. Um that's my own 541 00:31:53,160 --> 00:31:58,640 Speaker 1: personal opinion. But there are government organizations they're they're working 542 00:31:58,680 --> 00:32:01,840 Speaker 1: for the same people wore who are equally upset. Their 543 00:32:01,880 --> 00:32:05,160 Speaker 1: wheels within wheels would be the X files line. I mean, 544 00:32:05,200 --> 00:32:08,000 Speaker 1: you've got those, You've got those great rivalries between the 545 00:32:08,000 --> 00:32:10,400 Speaker 1: CIA and n s A. The date back to the 546 00:32:10,680 --> 00:32:15,840 Speaker 1: beginning of both organizations, and recently, as we're recording this, 547 00:32:16,360 --> 00:32:20,200 Speaker 1: more and more information about what we would call friendly 548 00:32:20,240 --> 00:32:26,640 Speaker 1: fire surveillance has leaked people who had not only the wherewithal, 549 00:32:26,800 --> 00:32:30,680 Speaker 1: but the motivation to keep their communications private or anonymous, 550 00:32:30,720 --> 00:32:34,440 Speaker 1: Like congres Members of Congress found that UM not only 551 00:32:34,520 --> 00:32:36,920 Speaker 1: was the n s A, but the FBI as well, 552 00:32:37,480 --> 00:32:43,120 Speaker 1: UH monitoring their monitoring their day to day emails and 553 00:32:43,160 --> 00:32:48,240 Speaker 1: phone calls, whatnot. The the thing that was was really 554 00:32:48,280 --> 00:32:51,680 Speaker 1: important to underline here is that it's not inherently sinister 555 00:32:52,200 --> 00:32:56,200 Speaker 1: to serve the web anonymously. And it's possible to do it, 556 00:32:56,280 --> 00:32:58,160 Speaker 1: as we said an earlier thing, but it's not really 557 00:32:58,240 --> 00:33:02,080 Speaker 1: plausible and not for a long term solution. No, No, 558 00:33:02,280 --> 00:33:06,000 Speaker 1: it's once off and we U we do tell you guys, 559 00:33:06,040 --> 00:33:09,760 Speaker 1: listeners in UH and Jonathan Lawns show, we show you 560 00:33:09,840 --> 00:33:15,760 Speaker 1: how theoretically you could make yourself, if not impossible to trace, 561 00:33:16,280 --> 00:33:20,400 Speaker 1: very very inconvenient to do so. Right. But it's basically 562 00:33:20,440 --> 00:33:25,480 Speaker 1: like like burner phone, burner, internet connection, burner face, like 563 00:33:25,560 --> 00:33:29,680 Speaker 1: you have to go, yeah, you gotta pretty much be uh. 564 00:33:29,840 --> 00:33:31,960 Speaker 1: You have to really limit what it is you want 565 00:33:32,000 --> 00:33:34,680 Speaker 1: to do, and you have to very much limit the 566 00:33:34,680 --> 00:33:36,880 Speaker 1: way you do it. So in other words, it's not 567 00:33:36,920 --> 00:33:41,040 Speaker 1: like you can just use that methodology to do everything 568 00:33:41,040 --> 00:33:42,960 Speaker 1: you would want to do on the web because there's 569 00:33:42,960 --> 00:33:45,120 Speaker 1: some cool stuff that's on the web that I love 570 00:33:45,200 --> 00:33:47,600 Speaker 1: to do that there's just no way to do anonymously, 571 00:33:47,880 --> 00:33:51,840 Speaker 1: not not truly right, like can you really have a 572 00:33:52,000 --> 00:33:56,520 Speaker 1: full decorgy watching experience if you can't log in and comment. 573 00:33:56,720 --> 00:34:00,440 Speaker 1: That also is a reference to the Text Stuff episode. 574 00:34:00,480 --> 00:34:04,400 Speaker 1: You'll learn way more about corky obsessions in that show. 575 00:34:04,640 --> 00:34:07,000 Speaker 1: I think it's enthusiasm. I don't think we've crossed the 576 00:34:07,040 --> 00:34:10,000 Speaker 1: line into obsession yet. We're just let me close out 577 00:34:10,040 --> 00:34:13,960 Speaker 1: a couple of taps. So while Jonathan's closing out a 578 00:34:13,960 --> 00:34:15,880 Speaker 1: couple of tabs, I do just want to set you 579 00:34:15,880 --> 00:34:19,440 Speaker 1: guys up for one more big question. UM, if you 580 00:34:19,560 --> 00:34:21,200 Speaker 1: if you like our show Stuff, then want you to 581 00:34:21,280 --> 00:34:24,799 Speaker 1: know listeners, then then you'll love tech Stuff because they 582 00:34:24,800 --> 00:34:30,120 Speaker 1: have also been talking about several different revelations, um, both 583 00:34:30,280 --> 00:34:33,000 Speaker 1: both with security and the nuts and bolts about how 584 00:34:33,040 --> 00:34:36,000 Speaker 1: these kind of things work. So we highly recommend their show. 585 00:34:36,520 --> 00:34:38,960 Speaker 1: And I have to ask you, guys, since you're the 586 00:34:38,960 --> 00:34:42,080 Speaker 1: ones with to know how UM, if you had to 587 00:34:42,200 --> 00:34:44,880 Speaker 1: guess or speculate, do you think that there would be 588 00:34:44,920 --> 00:34:49,960 Speaker 1: more news forthcoming like the whole Snowden disclosure thing where 589 00:34:50,000 --> 00:34:52,600 Speaker 1: he said, you know, he kicked down the door of 590 00:34:52,680 --> 00:34:58,040 Speaker 1: the news organizations and said this, but on everybody, Is 591 00:34:58,080 --> 00:35:00,719 Speaker 1: there anything else that would happen, because it seems like 592 00:35:00,840 --> 00:35:04,480 Speaker 1: that's the big Well, I mean, we only know what 593 00:35:04,560 --> 00:35:08,240 Speaker 1: we know, right, there's there's you can bet a couple 594 00:35:08,280 --> 00:35:12,080 Speaker 1: of things. You can bet that anything that has happened 595 00:35:12,280 --> 00:35:16,520 Speaker 1: since Snowdon has left is largely unknown to us because 596 00:35:16,800 --> 00:35:19,920 Speaker 1: he was the source of the leak. So anything that 597 00:35:20,000 --> 00:35:23,640 Speaker 1: has been done to address that or change things, evolve 598 00:35:23,800 --> 00:35:26,879 Speaker 1: the technologies that's being used, or or to find tune 599 00:35:26,920 --> 00:35:28,759 Speaker 1: them in different ways, or even apply them and even 600 00:35:28,760 --> 00:35:32,200 Speaker 1: more broad applications, or to fine tune the process by 601 00:35:32,200 --> 00:35:35,160 Speaker 1: which they make sure that other people don't link their information. Yeah, 602 00:35:35,200 --> 00:35:37,399 Speaker 1: all of that is unknown to us, so we can't 603 00:35:37,400 --> 00:35:39,279 Speaker 1: really be sure what's going on. What we do know 604 00:35:39,400 --> 00:35:42,399 Speaker 1: is just based upon the information that's been revealed so far. 605 00:35:42,920 --> 00:35:45,520 Speaker 1: There already have been abuses of the system. So that's 606 00:35:45,520 --> 00:35:48,200 Speaker 1: the other thing to keep in mind. Even if somehow 607 00:35:48,360 --> 00:35:51,960 Speaker 1: you could agree that the n s A system is 608 00:35:52,560 --> 00:35:55,480 Speaker 1: on its own, maybe you could call it flawed, but 609 00:35:55,520 --> 00:35:58,080 Speaker 1: it mostly works. Let's say that you even make that assumption, 610 00:35:58,480 --> 00:36:01,399 Speaker 1: the problem is it's run by people, and people, as 611 00:36:01,400 --> 00:36:04,520 Speaker 1: it turns out, our flawed very much so, and some 612 00:36:04,600 --> 00:36:09,160 Speaker 1: people will take advantage of having the opportunity to use 613 00:36:09,200 --> 00:36:11,840 Speaker 1: such a powerful tool to do things like snoop on 614 00:36:12,280 --> 00:36:16,000 Speaker 1: X girlfriends. Yeah, and even if someone isn't doing it 615 00:36:16,040 --> 00:36:20,279 Speaker 1: nefariously there there could certainly be mistakes made. Yeah. So 616 00:36:20,280 --> 00:36:22,320 Speaker 1: so there are a lot of issues that will probably 617 00:36:22,520 --> 00:36:28,319 Speaker 1: come to light as we get more people investigating this. Um. 618 00:36:28,360 --> 00:36:30,640 Speaker 1: The interesting thing to me is really seeing how much 619 00:36:30,680 --> 00:36:34,279 Speaker 1: movement we see in political circles to actually address this 620 00:36:34,320 --> 00:36:38,200 Speaker 1: in a meaningful way, because you do have lots of people, Uh, 621 00:36:38,320 --> 00:36:42,200 Speaker 1: you have lots of representatives who are at least saying 622 00:36:42,239 --> 00:36:47,560 Speaker 1: that they want more, more, more transparency because their constituents 623 00:36:47,560 --> 00:36:50,759 Speaker 1: are demanding it. Right, They're they're kind of demanding it too. 624 00:36:50,800 --> 00:36:53,719 Speaker 1: I mean it sounds like, yeah, once they found out 625 00:36:53,800 --> 00:36:58,160 Speaker 1: that they were also included. Yes, but uh there That's 626 00:36:58,200 --> 00:37:01,640 Speaker 1: one of the big debates always is uh, is it 627 00:37:01,719 --> 00:37:07,319 Speaker 1: a matter of sincere offense or fashionable offense fashionable indignation? 628 00:37:07,520 --> 00:37:10,760 Speaker 1: And and that's something that I think we will see 629 00:37:10,880 --> 00:37:15,399 Speaker 1: in the future with our listeners. We'd like to we'd 630 00:37:15,400 --> 00:37:18,200 Speaker 1: like to hear from you guys as well. What do 631 00:37:18,280 --> 00:37:24,960 Speaker 1: you think the next big revelations about the Internet would be? Um? Oh, 632 00:37:25,080 --> 00:37:28,720 Speaker 1: and here's one. Uh, can you or have you served 633 00:37:28,719 --> 00:37:32,239 Speaker 1: the web anonymously? Let's see if you could right in 634 00:37:32,360 --> 00:37:36,200 Speaker 1: and let us know and still stay anonymous. I don't know, 635 00:37:36,320 --> 00:37:38,920 Speaker 1: Let's just see if it works. Uh. In the meantime, 636 00:37:38,920 --> 00:37:41,560 Speaker 1: I'd like to thank Jonathan and Lauren you guys, thank 637 00:37:41,600 --> 00:37:44,719 Speaker 1: you so much for coming on our show. Um, I 638 00:37:44,760 --> 00:37:49,000 Speaker 1: wish I knew what had happened to Matt. We haven't 639 00:37:49,040 --> 00:37:50,640 Speaker 1: really said it on air, but if you want to 640 00:37:50,640 --> 00:37:53,520 Speaker 1: go ahead, and actually I can reveal at this point 641 00:37:53,560 --> 00:37:57,840 Speaker 1: that Matt in fact, was buried under a pile of 642 00:37:57,920 --> 00:38:02,080 Speaker 1: quirky puppies. And he's he's all right, but he's penned 643 00:38:02,120 --> 00:38:05,160 Speaker 1: and cannot move. He's the happiest that he has ever been. 644 00:38:05,280 --> 00:38:08,320 Speaker 1: He is stuck. He has been saying that I cannot 645 00:38:08,360 --> 00:38:12,600 Speaker 1: breathe and that's okay. Uh, in various languages. It's weird. 646 00:38:12,719 --> 00:38:16,520 Speaker 1: He actually is really fluent, but only in that one phrase. Yeah, 647 00:38:16,520 --> 00:38:19,479 Speaker 1: he's really smart, but it's strange that he only knows 648 00:38:19,520 --> 00:38:23,520 Speaker 1: that phrase. So, UM, I guess maybe I'll go try 649 00:38:23,600 --> 00:38:26,319 Speaker 1: to find him. And get him out because we still 650 00:38:26,360 --> 00:38:29,200 Speaker 1: need him for the show. Yeah, he's got some stuff 651 00:38:29,200 --> 00:38:32,879 Speaker 1: he needs to edit to and uh, and honestly, those 652 00:38:32,920 --> 00:38:34,879 Speaker 1: puppies are starting to get tired, and he just keeps 653 00:38:34,920 --> 00:38:36,600 Speaker 1: on picking up the ones that are wearing a nap 654 00:38:36,600 --> 00:38:40,120 Speaker 1: and putting him back on his stomach. So there's okay, 655 00:38:40,320 --> 00:38:42,920 Speaker 1: I know the pile of puppies you were talking about. Okay, No, 656 00:38:43,120 --> 00:38:50,640 Speaker 1: he's under there, yes, the third pile. Yes, yeah, okay. Great. Um. 657 00:38:50,880 --> 00:38:52,719 Speaker 1: As I said, guys, want to thank you so much 658 00:38:52,800 --> 00:38:56,120 Speaker 1: for coming on the show and being our very first guest. 659 00:38:56,800 --> 00:39:00,439 Speaker 1: I'd also like to let listeners know that if you 660 00:39:00,719 --> 00:39:03,120 Speaker 1: like this show, as you said, you'll enjoy tech stuff. 661 00:39:03,200 --> 00:39:06,200 Speaker 1: But these folks aren't just on tech stuff. They are 662 00:39:06,280 --> 00:39:10,480 Speaker 1: on another excellent podcast called Forward Thinking That which is 663 00:39:10,520 --> 00:39:14,239 Speaker 1: also a video series, and you can see all three 664 00:39:14,239 --> 00:39:17,200 Speaker 1: of us they think at various points, participating in everyday 665 00:39:17,239 --> 00:39:20,160 Speaker 1: science shananigans on a show called brain Stuff. You can 666 00:39:20,200 --> 00:39:22,719 Speaker 1: actually see all three of us in in the episode 667 00:39:22,760 --> 00:39:28,320 Speaker 1: about about product placement. Oh boy, that one I forgot 668 00:39:28,320 --> 00:39:31,160 Speaker 1: about that. Yeah. Well, if you want to see why 669 00:39:31,239 --> 00:39:33,680 Speaker 1: they're laughing at me, you can check that one out 670 00:39:33,760 --> 00:39:36,480 Speaker 1: to Uh, you can find Stuff they Don't Want You 671 00:39:36,560 --> 00:39:41,359 Speaker 1: to Know dot com for every video and every podcast 672 00:39:41,480 --> 00:39:43,960 Speaker 1: we've ever made, and of course we're all over the internet. 673 00:39:44,000 --> 00:39:46,640 Speaker 1: You can drop us a line with a suggestion or 674 00:39:46,719 --> 00:39:49,319 Speaker 1: feedback on Twitter or Facebook. That's where we put a 675 00:39:49,320 --> 00:39:51,440 Speaker 1: lot of the stories that don't make it into videos 676 00:39:51,480 --> 00:39:54,080 Speaker 1: or podcasts, So do check it out. And that's the 677 00:39:54,200 --> 00:39:57,600 Speaker 1: end of this classic episode. If you have any thoughts 678 00:39:57,719 --> 00:40:01,120 Speaker 1: or questions about this episode mode, you can get into 679 00:40:01,160 --> 00:40:03,719 Speaker 1: contact with us in a number of different ways. One 680 00:40:03,719 --> 00:40:05,279 Speaker 1: of the best is to give us a call. Our 681 00:40:05,360 --> 00:40:09,279 Speaker 1: number is one eight three three std w y t K. 682 00:40:09,800 --> 00:40:11,600 Speaker 1: If you don't want to do that, you can send 683 00:40:11,640 --> 00:40:14,920 Speaker 1: us a good old fashioned email. We are conspiracy at 684 00:40:14,920 --> 00:40:18,560 Speaker 1: i heart radio dot com. Stuff they Don't Want You 685 00:40:18,600 --> 00:40:21,239 Speaker 1: to Know is a production of I Heart Radio. For 686 00:40:21,320 --> 00:40:23,680 Speaker 1: more podcasts from my heart Radio, visit the i heart 687 00:40:23,760 --> 00:40:26,560 Speaker 1: Radio app, Apple Podcasts, or wherever you listen to your 688 00:40:26,560 --> 00:40:27,240 Speaker 1: favorite shows.