#pseudonymity — Public Fediverse posts

Pook-Emu Bee: Links For 05-18-26

https://social.emucafe.org/naferrell/pook-emu-bee-links-for-05-18-26/

Pook-Emu Bee: Links For 05-18-26

https://social.emucafe.org/naferrell/pook-emu-bee-links-for-05-18-26/

Is “Satoshi Nakamoto” Really Adam Back?

The New York Times has a long article where the author lays out an impressive array of circumstantial evidence that the inventor of B... https://www.schneier.com/blog/archives/2026/04/is-satoshi-nakamoto-really-adam-back.html

#cryptocurrency #Uncategorized #pseudonymity #bitcoin

Working 'anonymously or under a pseudonym serves vital societal interests,' Banksy's lawyer Mark Stephens wrote.

https://www.abc.net.au/news/2026-03-17/banksy-real-identity-revealed-in-new-investigation/106462888

'It protects freedom of expression by allowing creators to speak truth to power without fear of retaliation, censorship or persecution — particularly when addressing sensitive issues such as politics, religion or social justice.'

#streetart #Banksy #anonymity #pseudonymity

Large-scale online deanonymization with LLMs
From Cornel University Computer Science

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè

https://arxiv.org/abs/2602

#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization

Large-scale online deanonymization with LLMs
From Cornel University Computer Science

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè

https://arxiv.org/abs/2602

#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization

Large-scale online deanonymization with LLMs
From Cornel University Computer Science

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè

https://arxiv.org/abs/2602

#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization

Large-scale online deanonymization with LLMs
From Cornel University Computer Science

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè

https://arxiv.org/abs/2602

#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization

Large-scale online deanonymization with LLMs
From Cornel University Computer Science

We show that large language models can be used to perform at-scale deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline that uses LLMs to: (1) extract identity-relevant features, (2) search for candidate matches via semantic embeddings, and (3) reason over top candidates to verify matches and reduce false positives. Compared to classical deanonymization work (e.g., on the Netflix prize) that required structured data, our approach works directly on raw user content across arbitrary platforms. We construct three datasets with known ground-truth data to evaluate our attacks. The first links Hacker News to LinkedIn profiles, using cross-platform references that appear in the profiles. Our second dataset matches users across Reddit movie discussion communities; and the third splits a single user's Reddit history in time to create two pseudonymous profiles to be matched. In each setting, LLM-based methods substantially outperform classical baselines, achieving up to 68% recall at 90% precision compared to near 0% for the best non-LLM method. Our results show that the practical obscurity protecting pseudonymous users online no longer holds and that threat models for online privacy need to be reconsidered.

Simon Lermen, Daniel Paleka, Joshua Swanson, Michael Aerni, Nicholas Carlini, Florian Tramè

https://arxiv.org/abs/2602

#computerscience #cornelluniversity #AiResearch #privacy #anonymity #llm #HackNews #athropic #pseudonymity
#deanonymization

You've got nothing to hide, do you?

»We show that large language models can be used to perform at-scale #deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline«

https://arxiv.org/abs/2602.16800

"#AI" #privacy #pseudonymity #anonymity #LLM

You've got nothing to hide, do you?

»We show that large language models can be used to perform at-scale #deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline«

https://arxiv.org/abs/2602.16800

"#AI" #privacy #pseudonymity #anonymity #LLM

You've got nothing to hide, do you?

»We show that large language models can be used to perform at-scale #deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline«

https://arxiv.org/abs/2602.16800

"#AI" #privacy #pseudonymity #anonymity #LLM

You've got nothing to hide, do you?

»We show that large language models can be used to perform at-scale #deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline«

https://arxiv.org/abs/2602.16800

"#AI" #privacy #pseudonymity #anonymity #LLM

You've got nothing to hide, do you?

»We show that large language models can be used to perform at-scale #deanonymization. With full Internet access, our agent can re-identify Hacker News users and Anthropic Interviewer participants at high precision, given pseudonymous online profiles and conversations alone, matching what would take hours for a dedicated human investigator. We then design attacks for the closed-world setting. Given two databases of pseudonymous individuals, each containing unstructured text written by or about that individual, we implement a scalable attack pipeline«

https://arxiv.org/abs/2602.16800

"#AI" #privacy #pseudonymity #anonymity #LLM

LLMs can break online #pseudonymity and identify users across platforms

https://cyberinsider.com/llms-can-break-online-pseudonymity-and-identify-users-across-platforms/

#LLM #AI #privacy

Pseudonymity saves lives.
It is vital to healthy democracies.

#Privacy #Pseudonymity #HumanRights

New Privacy Guides video 🎞️🪪
by @jw:

Age Verification represents an incredible threat to our privacy.

Not only Age Verification doesn't protect the children, but this could mean the end of protective pseudonymity for everyone if implemented widely.

Watch this excellent video created by Jordan here on PeerTube (based on my article on the same topic): https://neat.tube/w/aR4toTWJpcBZamUdQQpGRu

#PrivacyGuides #Privacy #AgeVerification #DataMinimization #Pseudonymity #PeerTube

New Privacy Guides video 🎞️🪪
by @jw:

Age Verification represents an incredible threat to our privacy.

Not only Age Verification doesn't protect the children, but this could mean the end of protective pseudonymity for everyone if implemented widely.

Watch this excellent video created by Jordan here on PeerTube (based on my article on the same topic): https://neat.tube/w/aR4toTWJpcBZamUdQQpGRu

#PrivacyGuides #Privacy #AgeVerification #DataMinimization #Pseudonymity #PeerTube

New Privacy Guides video 🎞️🪪
by @jw:

Age Verification represents an incredible threat to our privacy.

Not only Age Verification doesn't protect the children, but this could mean the end of protective pseudonymity for everyone if implemented widely.

Watch this excellent video created by Jordan here on PeerTube (based on my article on the same topic): https://neat.tube/w/aR4toTWJpcBZamUdQQpGRu

#PrivacyGuides #Privacy #AgeVerification #DataMinimization #Pseudonymity #PeerTube

New Privacy Guides video 🎞️🪪
by @jw:

Age Verification represents an incredible threat to our privacy.

Not only Age Verification doesn't protect the children, but this could mean the end of protective pseudonymity for everyone if implemented widely.

Watch this excellent video created by Jordan here on PeerTube (based on my article on the same topic): https://neat.tube/w/aR4toTWJpcBZamUdQQpGRu

#PrivacyGuides #Privacy #AgeVerification #DataMinimization #Pseudonymity #PeerTube

New Privacy Guides video 🎞️🪪
by @jw:

Age Verification represents an incredible threat to our privacy.

Not only Age Verification doesn't protect the children, but this could mean the end of protective pseudonymity for everyone if implemented widely.

Watch this excellent video created by Jordan here on PeerTube (based on my article on the same topic): https://neat.tube/w/aR4toTWJpcBZamUdQQpGRu

#PrivacyGuides #Privacy #AgeVerification #DataMinimization #Pseudonymity #PeerTube

#anonymity #pseudonymity #anonymat #pseudonymat

The Decoupling Principle

This is a really interesting paper that discusses what the authors call the Decoupling Principle:
The idea is simple, yet previously not cle... https://www.schneier.com/blog/archives/2022/12/the-decoupling-principle.html

#academicpapers #authentication #Uncategorized #pseudonymity #anonymity #privacy

The Decoupling Principle https://www.schneier.com/blog/archives/2022/12/the-decoupling-principle.html #academicpapers #authentication #Uncategorized #pseudonymity #anonymity #privacy

Question for more experienced Mastodon users. I've just installed Fedilab on my phone and have added both my Masto accounts (this and my SWer account). They're on different instances. Not following each other. I want to keep them separate.

But in Fedilab posts from each show up on the other's home feed. It's alarming. Does that mean the profiles are linked?

My #pseudonymity and #onlineprivacy are crucially important to my wellbeing and safety, so this is worrying.

Let's face it: people seek #pseudonymity (which is NOT #anonymity) not because they detest influencers, but simply because they are afraid of ambiguous consequences over what they say. Look at all the interviewees: none of them have outrageous opinions, yet all have concerns over job prospects, so deep down we all know that employers will dig through online profiles, and that's a problem. We need serious talks on #privacy, not treating it as a "trend."

https://www.theatlantic.com/technology/archive/2022/06/gen-z-internet-anonymity-instagram-tumblr/661316/

@vortex_egg There's a very long and substantial history of people adopting pseudonyms, nicknames, pen names, stage names, nommes de guerre, etc., for any number of reasons.

Some you're no doubt aware of, many you're quite likely not. ("Willy Brandt" was a particular surprise for me.)

https://en.wikipedia.org/wiki/Pseudonym
https://en.wikipedia.org/wiki/List_of_pseudonyms
https://en.wikipedia.org/wiki/List_of_pen_names
https://en.wikipedia.org/wiki/List_of_stage_names
https://en.wikipedia.org/wiki/List_of_pseudonyms_used_in_the_American_Constitutional_debates
https://en.wikipedia.org/wiki/Mononymous_person

#pseydonyms #pseudonymity #pseudonymous #PenNames #StageNames #NommesDeGuerre #Anonymity #Identity

@vortex_egg There's a very long and substantial history of people adopting pseudonyms, nicknames, pen names, stage names, nommes de guerre, etc., for any number of reasons.

Some you're no doubt aware of, many you're quite likely not. ("Willy Brandt" was a particular surprise for me.)

https://en.wikipedia.org/wiki/Pseudonym
https://en.wikipedia.org/wiki/List_of_pseudonyms
https://en.wikipedia.org/wiki/List_of_pen_names
https://en.wikipedia.org/wiki/List_of_stage_names
https://en.wikipedia.org/wiki/List_of_pseudonyms_used_in_the_American_Constitutional_debates
https://en.wikipedia.org/wiki/Mononymous_person

#pseydonyms #pseudonymity #pseudonymous #PenNames #StageNames #NommesDeGuerre #Anonymity #Identity

De-anonymization Story https://www.schneier.com/blog/archives/2021/07/de-anonymization-story.html #de-anonymization #datacollection #Uncategorized #pseudonymity #surveillance #geolocation #privacy

Forced revelation of information makes individual privilege more important (2014)

In practice, the forced revelation of information makes individual privilege and power more important. When everyone has to play with their cards on the table, so to speak, then people who feel like they can be themselves without consequence do so freely -- these generally being people with support groups of like-minded people, and who are neither economically nor physically vulnerable. People who are more vulnerable to consequences use concealment as a method of protection: it makes it possible to speak freely about controversial subjects, or even about any subjects, without fear of harassment.

-- Yonatan Zunger, chief architect of Google+

HN discussion: https://news.ycombinator.com/item?id=27858439

#RealNames #anonymity #pseudonymity #identity #power #PowerRelationships #YonatanZunger #GooglePlus #DavidBrin #TransparentSociety