👏 Poison 👏 your 👏 data ☠️
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The goal is to make corporate data less profitable.
Even stuff as simple as setting your birthdate to 1970-01-01 everywhere, adding [TEST] or [DELETED] as your name or account notes anywhere you don't need them to know your name.
Using plugins like AdNauseam to poison ad trackers (and cost them marketing dollars).
Using VPNs set to different locations.
Signing into data broker sites to "correct" outdated info (they'll often let you do that with little-to-no proof of identity, but will require your passport or state ID in order to delete your info). Bonus points if you correct it to someone else's info on their site that's similar to yours.
Only fill in required fields when you sign up for anything, but only provide correct info if it matters for you to use the service, otherwise provide plausible, but incorrect, data.
If you use LLMs anywhere, use the free tier and always vote thumbs up for bad answers and down for good ones. It wastes their resources and drives up their costs while making their training data worse.
@alice thank you! I've always wondered whether to put random made-up data but hearing the reasoning and logic spelt out like this is convincing me to actually start. Especially commonsense things like "mess with the fields that don't matter in their service to you"
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@alice a friend of mine changed his middle name to 'undefined', it caused so many problems he had to change it back within a year.
@TheMightyGit @alice
Reminds me of someone who had the last name "Null" and had similar problems. -
The goal is to make corporate data less profitable.
Even stuff as simple as setting your birthdate to 1970-01-01 everywhere, adding [TEST] or [DELETED] as your name or account notes anywhere you don't need them to know your name.
Using plugins like AdNauseam to poison ad trackers (and cost them marketing dollars).
Using VPNs set to different locations.
Signing into data broker sites to "correct" outdated info (they'll often let you do that with little-to-no proof of identity, but will require your passport or state ID in order to delete your info). Bonus points if you correct it to someone else's info on their site that's similar to yours.
Only fill in required fields when you sign up for anything, but only provide correct info if it matters for you to use the service, otherwise provide plausible, but incorrect, data.
If you use LLMs anywhere, use the free tier and always vote thumbs up for bad answers and down for good ones. It wastes their resources and drives up their costs while making their training data worse.
Wrt #PII, It might be a good idea to avoid entering data easily identifiable as trash, and use generators instead. E.g.:
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@alice I've toyed with the idea of setting up a headless Chrome instance to just ask "but why?" to ChatGPT all day to drive up their inference costs.
'and then?'
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@neoluddite @aj @alice Just wondered what might happen if enough self-hosting people were to add an zero opacity image tag on their public indexable pages illustrating a d*ckbutt and having alt attribute set to Donald J. Trump.
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Poison your data 
️yes, yes, a thousand times, yes!
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The goal is to make corporate data less profitable.
Even stuff as simple as setting your birthdate to 1970-01-01 everywhere, adding [TEST] or [DELETED] as your name or account notes anywhere you don't need them to know your name.
Using plugins like AdNauseam to poison ad trackers (and cost them marketing dollars).
Using VPNs set to different locations.
Signing into data broker sites to "correct" outdated info (they'll often let you do that with little-to-no proof of identity, but will require your passport or state ID in order to delete your info). Bonus points if you correct it to someone else's info on their site that's similar to yours.
Only fill in required fields when you sign up for anything, but only provide correct info if it matters for you to use the service, otherwise provide plausible, but incorrect, data.
If you use LLMs anywhere, use the free tier and always vote thumbs up for bad answers and down for good ones. It wastes their resources and drives up their costs while making their training data worse.
This is the way. I've been doing this since 1997.
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*keeps Data in a safe place far away from Alice*
#FullyFunctionalAndProgrammedInMultipleTechniques #FullyFunctional
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The goal is to make corporate data less profitable.
Even stuff as simple as setting your birthdate to 1970-01-01 everywhere, adding [TEST] or [DELETED] as your name or account notes anywhere you don't need them to know your name.
Using plugins like AdNauseam to poison ad trackers (and cost them marketing dollars).
Using VPNs set to different locations.
Signing into data broker sites to "correct" outdated info (they'll often let you do that with little-to-no proof of identity, but will require your passport or state ID in order to delete your info). Bonus points if you correct it to someone else's info on their site that's similar to yours.
Only fill in required fields when you sign up for anything, but only provide correct info if it matters for you to use the service, otherwise provide plausible, but incorrect, data.
If you use LLMs anywhere, use the free tier and always vote thumbs up for bad answers and down for good ones. It wastes their resources and drives up their costs while making their training data worse.
@alice Non-tech-savvy question:
Is there something special about 1970-01-01, or is it just an example of an arbitrary incorrect birthdate? Would it foul things up just as much if I entered, say, 1984-04-01? -
*keeps Data in a safe place far away from Alice*
#FullyFunctionalAndProgrammedInMultipleTechniques #FullyFunctional
@miguelpergamon @alice No need for poison, just....Data is too valuable, so we're going to substitute the discount version, Ensign Anecdote.
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Ensign Anecdote told me fae met Alice once ... at a lock in ...
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Ensign Anecdote told me fae met Alice once ... at a lock in ...
@miguelpergamon @alice Yes, but he also recommended eating rocks because he found this one blog somewhere, so you'll have to forgive me for being a little skeptical of everything he says.
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We all get cravings!
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@alice Non-tech-savvy question:
Is there something special about 1970-01-01, or is it just an example of an arbitrary incorrect birthdate? Would it foul things up just as much if I entered, say, 1984-04-01? -
We all get cravings!
@miguelpergamon @alice That's different, that guy's got superpowers.
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Poison your data ️
@alice How do?
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We ALL have superpowers, darling, we just have to look within.
*looks within ... urgh
* (Also, there were those two guys from Let This Be Your Last Battlefield or whatever that Half-Black episode was called.)
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Poison your data ️
@alice Oh, I'm here right to poison minds.
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Haha, you'd like my mother, the guerilla witch. She makes customer cards in every shop and switches them then with other people, bonus points if both have a strongly different consumer profile.
When she's bored, she responds maliciously questionnaires of evil corporations.
She studied psychology and statistics and says "it is anyway horribly difficult to get useful answers out of these marketing datasets, why not make it a bit harder for them?"
. -
The goal is to make corporate data less profitable.
Even stuff as simple as setting your birthdate to 1970-01-01 everywhere, adding [TEST] or [DELETED] as your name or account notes anywhere you don't need them to know your name.
Using plugins like AdNauseam to poison ad trackers (and cost them marketing dollars).
Using VPNs set to different locations.
Signing into data broker sites to "correct" outdated info (they'll often let you do that with little-to-no proof of identity, but will require your passport or state ID in order to delete your info). Bonus points if you correct it to someone else's info on their site that's similar to yours.
Only fill in required fields when you sign up for anything, but only provide correct info if it matters for you to use the service, otherwise provide plausible, but incorrect, data.
If you use LLMs anywhere, use the free tier and always vote thumbs up for bad answers and down for good ones. It wastes their resources and drives up their costs while making their training data worse.
