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Re:Attacks against machine learning – an overview by bhnmmhmd on Sunday June 10, 2018 @04:06PM (#17279911)
It'd be great if there was a service that you could sign up for, which would "deceive" Facebook, Twitter, and other social media websites by producing false information about you. For example, if I don't want FB to know what movies I'm interested in, how about liking "random" movie pages on FB? If I don't want FB to know about my political orientations, how about run with the hare and hunt with the hounds?
Re:Attacks against machine learning – an overview by nihonde on Sunday June 10, 2018 @06:07PM (#17280435)
This is very much the same foundation of disinformation campaigns like strongarm regime propaganda or what is currently termed “fake news”. By attacking credibility itself, anything and nothing are equally valid. The problem with poisoning the well of your own personal data is that it also makes it easier to indict you on false pretenses.
Re:Attacks against machine learning – an overview by dman on Sunday June 10, 2018 @09:14PM (#17281070)
I guess if enough number of people do it, it removes the ability to indict anyone.
Re:Attacks against machine learning – an overview by joshgel on Sunday June 10, 2018 @11:28PM (#17281525)
Or make it so anyone can be hunted for no reason at all
Re:Attacks against machine learning – an overview by itronitron on Sunday June 10, 2018 @11:51PM (#17281627)
indict you for what?
Re:Attacks against machine learning – an overview by girvo on Monday June 11, 2018 @05:49AM (#17283025)
Whatever they decide to, I believe is the GPs point. When nothing is true, everything can be “true”
Re:Attacks against machine learning – an overview by cinquemb on Monday June 11, 2018 @05:54AM (#17283048)
However, if people are already doing something that is deemed illegal or not allowed by any given jurisdiction/site, making the pool of people to theoretically go after/ban order of magnitude(s) larger seems ideal.
Re:Attacks against machine learning – an overview by jacquesm on Sunday June 10, 2018 @05:15PM (#17280177)
Not a happy answer but such a service would raise the noise floor but would otherwise not have much of an effect unless it was massively adopted to the point that the original signal was insignificant compared to the noise.
Re:Attacks against machine learning – an overview by mlthoughts2018 on Sunday June 10, 2018 @08:41PM (#17280948)
Even then it’s questionable if it would be effective. Over time even a faint behavioral signature will become transparent, because things that deviate from the true behavioral signature do so in random ways, which could essentially “cancel” out if your model for quantifying behavioral characteristics is well-specified. Meanwhile the “true” behaviors would “add” over time.

It would become like any other signal jamming arms race, whether it’s radar or social behaviors, and your model of generating random noise has to get more sophisticated as the other party’s anti-jamming techniques get more sophisticated.

I took a class with Scott Aaronson once where he mentioned the idea that the natural enemy of machine learning is cryptography.

So if you know the anti-jammers are using ever greater machine learning techniques, rather than trying to one-up them with adversarial learning, I suspect the best jamming would be cryptography.

Like, extensions to Facebook that essential encode text with PGP or something, send via Messenger, and allow decoding on the other side.

Then an interesting idea for machine learning would be how to make an autoencoder that accepts encrypted text, transforms it into human understandable text that would fool a machine learning algorithm designed to flag encrypted text, and can decode from natural language back to the encrypted data on the other end.

Re:Attacks against machine learning – an overview by gwern on Monday June 11, 2018 @10:19AM (#17284405)
> I took a class with Scott Aaronson once where he mentioned the idea that the natural enemy of machine learning is cryptography.

He cites Rivest for that one: https://people.csail.mit.edu/rivest/pubs/Riv91.pdf

Re:Attacks against machine learning – an overview by JetSpiegel on Sunday June 10, 2018 @04:13PM (#17279943)
AdNauseum[1] is that "service", but Google blocked it from the Chorome Add On Store for obvious reasons.

[1]: https://adnauseam.io/

Re:Attacks against machine learning – an overview by Deinos on Sunday June 10, 2018 @05:14PM (#17280173)
One of the many reasons I switched over to Firefox. Additionally, AdNauseum is an amazing product name.
Re:Attacks against machine learning – an overview by bhnmmhmd on Sunday June 10, 2018 @07:36PM (#17280743)
Another reason: Stylus (FF-only) vs. Stylish (which works on both FF and Chrome, but collects your data).
Re:Attacks against machine learning – an overview by aportnoy on Sunday June 10, 2018 @06:47PM (#17280597)
*AdNauseam
Re:Attacks against machine learning – an overview by delbel on Sunday June 10, 2018 @05:49PM (#17280350)
I suggested this to an EFF lawyer at Defcon and they hated the idea. Perhaps I worded it wrong, it makes sense to me. Signal to noise ratio. You should have a legal right to submit false information if you feel a service might harm you at some point.
Re:Attacks against machine learning – an overview by kbrackbill on Sunday June 10, 2018 @05:54PM (#17280375)
Interesting, what did they hate about it?
Re:Attacks against machine learning – an overview by ice_nine on Sunday June 10, 2018 @07:44PM (#17280764)
Not OP but if I had to guess, once you have a 'legal right' to submit false information it paves the way for a lot of unwanted behavior and would help the spread of disinformation. If a service is harming you and you're thinking about ways to legally address the issue, why not just go after the service itself and come up with legal repercussions / regulations for their actions?

Your question led me to this NPR article that briefly talks about the legality of lying on the Internet that's worth mentioning (https://www.npr.org/sections/thetwo-way/2011/11/15/142356399...). It seems that when you agree to the Terms of Service with services like Facebook, you agree to not spread misinformation or misrepresent yourself (https://www.facebook.com/communitystandards/integrity_authen...).

Re:Attacks against machine learning – an overview by itronitron on Sunday June 10, 2018 @11:57PM (#17281641)
In my opinion, it isn't even information in the first place, so it can't be categorized into true or false information buckets. There are way too many conclusions being drawn from the stuff that gets tossed through the digital ether.
Re:Attacks against machine learning – an overview by hellbanner on Sunday June 10, 2018 @07:58PM (#17280806)
Apple could file a patent infringement suit if you tried to automate it.

https://www.digitaltrends.com/apple/apple-gets-privacy-prote...

Re:Attacks against machine learning – an overview by ButterflyWar on Monday June 11, 2018 @12:01AM (#17281660)
Re:Attacks against machine learning – an overview by lozaning on Sunday June 10, 2018 @10:24PM (#17281286)
I use this nift browser plug in: https://noiszy.com/

"It visits and navigates around websites, from within your browser, leaving misleading digital footprints around the internet. Noiszy only visits a list of sites that you approve, and only works when you turn it on. Run Noiszy in the background while you're working, or start Noiszy when you're not using your browser, and it sends meaningless data to these sites for as long as you let it run."

Re:Attacks against machine learning – an overview by itronitron on Sunday June 10, 2018 @11:50PM (#17281622)
In order to wash out the signal, all the service would need to do is 'like everything'. In addition to masking your interests it would also grind their algorithms to a halt if enough people did that. A lot of these algorithms gain performance due to the sparsity of the data, so if everything became connected it would negatively impact the performance of their algorithms.

Anyone know how to get, or compile, a list of everything likable on Facebook?

Re:Attacks against machine learning – an overview by pferde on Monday June 11, 2018 @03:57AM (#17282576)
Until you get into problems (legal or personal, doesn't matter) for "liking" stuff related to child porn, terrorist propaganda or, I don't know, scientology, without even knowing about it, because it was done on your behalf by this "like automaton".
Re:Attacks against machine learning – an overview by sacado2 on Monday June 11, 2018 @06:51AM (#17283227)
Exactly. In France people have been convicted because they "liked" illegal opinions. As if the fact that such a thing as an illegal opinion exists was not enough of a problem, it's been decided by justice that the semantic of a "like" was "I make this opinion mine".
Re:Attacks against machine learning – an overview by pferde on Monday June 11, 2018 @07:51AM (#17283479)
Can you please share some links on this? All I could find was a similar case in Thailand: https://www.theguardian.com/world/2015/dec/10/thai-man-arres...
Re:Attacks against machine learning – an overview by sacado2 on Monday June 11, 2018 @09:07AM (#17283849)
I found that, but this is in French, obviously: http://www.leparisien.fr/rozay-en-brie-77540/rozay-en-brie-c...

My own (approximate) translation of parts of the text:

"Sur Facebook, le trentenaire avait apposé un «J’aime» sur une image d’un combattant de Daesh brandissant la tête décapitée d’une femme. Il a été condamné à trois mois de prison avec sursis." --> "On Facebook, the man in his thirties had clicked "like" on a picture of an ISIS fighter holding the head of a beheaded woman. He was given a 3-month suspended prison sentence".

"«Quand on met J’aime, c’est que l’on considère que ce n’est pas choquant ou que l’on adhère», considère pour sa part Jean-Baptiste Bougerol, le substitut du procureur de la République." --> ""When you click "like" on something, you consider it's not shocking or you agree with it"", said the prosecutor".

Re:Attacks against machine learning – an overview by itronitron on Monday June 11, 2018 @06:39PM (#17289248)
anything likable on Facebook shouldn't be illegal, or illegal to like
Re:Attacks against machine learning – an overview by sacado2 on Monday June 11, 2018 @06:54AM (#17283235)
If you like everything, or if you say "my birthday is the 32nd of February", the algorithm can detect you're trying to defeat the system, and ignore you: you become a known unknown.

But if you start to like random things, or if you say "my birthdate is the 2nd of March" while it's not, you become an unknown unknown, and the algorithm must start to reason with your wrong data.

Re:Attacks against machine learning – an overview by lwansbrough on Monday June 11, 2018 @12:32AM (#17281810)
Facebook uses an enumerable ID for everything public, so to get a list you just have to enumerate their public pages.
Re:Attacks against machine learning – an overview by baxtr on Sunday June 10, 2018 @04:21PM (#17279973)
I actually had the strategy on fb to just like very random stuff like movies, groups etc. I’m not sure whether it has helped but it makes me feel better
Re:Attacks against machine learning – an overview by kinsomo on Sunday June 10, 2018 @08:00PM (#17280812)
> I actually had the strategy on fb to just like very random stuff like movies, groups etc. I’m not sure whether it has helped but it makes me feel better

I've done similar, and afterwards nearly all advertising categorizations of me eventually dropped off my profile (after a period where they were schizophrenic and contradictory). I can't be certain I caused that, because this was contemporaneous with Zuck's congressional testimony and the run up to the GDPR (both of which probably motivated many changes).

But it would make sense that mountains of bad data would make it hard for them to confidently place me in advertising demographic and interest categories, do to all the contradictions.

Re:Attacks against machine learning – an overview by spunker540 on Sunday June 10, 2018 @06:31PM (#17280532)
How about you don’t sign up for Facebook if you don’t want them to know anything about you? I don’t really see the point of this deception.
Re:Attacks against machine learning – an overview by MereInterest on Sunday June 10, 2018 @06:36PM (#17280553)
Because facebook tracks you, even if you do not have a facebook account.

https://www.theverge.com/2018/4/11/17225482/facebook-shadow-...

Re:Attacks against machine learning – an overview by mortehu on Sunday June 10, 2018 @10:20PM (#17281272)
Because the word "tracking" can include request monitoring for DDoS mitigation and scraping detection, or storing information purposefully uploaded by other people (e.g. photos and contact list), saying anything about "tracking" isn't very meaningful.

Think of how many people are being involuntarily "tracked" by Dropbox because others are backing up photos in which they appear, or emails they sent, without their consent. For better headlines, we could call this information "Dropbox Shadow Dossiers".

Re:Attacks against machine learning – an overview by JumpCrisscross on Sunday June 10, 2018 @06:48PM (#17280601)
The problem is laziness. “Call your Congressperson” is hard. Waxing lyrical about political distinction is easy. Imagining technical solutions to political problems is easier.
Re:Attacks against machine learning – an overview by MereInterest on Sunday June 10, 2018 @07:11PM (#17280655)
I'd say that there are two related problems, one political and one technical, and that there is no reason not to address both issues.

* Political problem: It is legal and acceptable to track people on the internet to an extreme degree. Political solution: Call your Congressperson, donate to the EFF, reframe the issue as corporate stalking, etc.

* Technical problem: It is possible to track people on the internet to an extreme degree. Technical solution: Restrict ability to collect data by using adblock, poison existing databases with reasonable but false data.

Re:Attacks against machine learning – an overview by dominotw on Sunday June 10, 2018 @07:34PM (#17280729)
is it possible to block facebook domain via hosts file or chrome extension?
Re:Attacks against machine learning – an overview by itronitron on Monday June 11, 2018 @12:00AM (#17281653)
Re:Attacks against machine learning – an overview by sacado2 on Monday June 11, 2018 @06:59AM (#17283257)
Most of your friends, family, coworkers, etc. have your e-mail or your phone number (or both). Some (most) of them use facebook, and a significative portion of them share those information with facebook, so even if you never subscribe to the service, they know a few things about you (your name, phone number, e-mail address and some of your acquaintances), and they know they don't know anything else about you (which is some sort of information too).
Re:Attacks against machine learning – an overview by lurquer on Monday June 11, 2018 @06:13PM (#17289088)
In the late 90s-early 00s, at a corporate job, several of us used a script to have the browser load a random page every few minutes. (I didn't write the script... I think the pages were just random links from search results based on random words.) Anyway, the thousands of pages loaded masked the NSFW pages. We figured we'd have plausible deniability. "You say I visited Xxx.com? I don't know... I think my computer has a virus or something. It's always loading up random stuff. Let me see the log... Yep... Just as I thought... It says I visited 24,239 pages on Tuesday. Heck, that's not even possible!"
Re:Attacks against machine learning – an overview by theapemachine on Monday June 11, 2018 @04:39AM (#17282750)
It seems unlikely that this would be 100% effective though. I am pretty sure that companies like this use data from your immediate social sphere as well to make pretty relevant assumptions about you. For instance, when I buy a product, certain others of my friends will see this same product promoted to them. My friend once looked something up on Facebook, while we were in the movie theater (before the movie started ;)) and sure enough I was getting the ads for that same event as soon as I got home and looked at my phone. Of course, on a more massive scale, this could theoretically work.
Re:Attacks against machine learning – an overview by startupdiscuss on Sunday June 10, 2018 @04:22PM (#17279975)
Could you just give fake info? Or else use multiple fake profiles?

I don't know how you an deceive Facebook without also deceiving your friends and contacts though.

Re:Attacks against machine learning – an overview by SiempreViernes on Sunday June 10, 2018 @04:30PM (#17280003)
Does the api support creating old posts and back dating them?

FB is really good at hiding old activity and being utterly worthless at searching your feed, so if you can just put all the fake stuff in the past you'd be fine with your real friends.

Re:Attacks against machine learning – an overview by kinsomo on Sunday June 10, 2018 @08:03PM (#17280826)
> Does the api support creating old posts and back dating them?

> FB is really good at hiding old activity and being utterly worthless at searching your feed, so if you can just put all the fake stuff in the past you'd be fine with your real friends.

They allow you to back date, but if you're goal is to avoid annoying your friends, you could use the privacy settings for a similar effect. Just post your garbage as visible to "only me," let it age for a week or two until the algorithm will ignore it, then make it "public," "friends only," or whatever you want.

Re:Attacks against machine learning – an overview by poulsbohemian on Monday June 11, 2018 @06:02PM (#17289012)
What's funny is how bad most of these services are at identifying people. They seem to think I'm a southeast Asian female (white dude here) because I happen to read a particular article, buy a particular item (probably as a gift), etc. Turn on, tune in, drop out...
Re:Attacks against machine learning – an overview by hellbanner on Sunday June 10, 2018 @07:57PM (#17280804)