Tootfinder

On the $(k,\ell)$-multiset anonymity measure for social graphs
Alejandro Estrada-Moreno, Elena Fern\'andez, Dorota Kuziak, Manuel Mu\~noz-M\'arquez, Rolando Trujillo-Rasua, Ismael G. Yero
https://arxiv.org/abs/2507.08433

On the $(k,\ell)$-multiset anonymity measure for social graphs
The publication of social graphs must be preceded by a rigorous analysis of privacy threats against social graph users. When the threat comes from inside the social network itself, the threat is called an active attack, and the de-facto privacy measure used to quantify the resistance to such an attack is the $(k,\ell)$-anonymity. The original formulation of $(k,\ell)$-anonymity represents the adversary's knowledge as a vector of distances to the set of attacker nodes. In this article, we argue …

Randomly reminded of the pet mice my family had when I was a kid.
We'd always get them in pairs and they had the weirdest names.
I've probably forgot a few but...
Breyah and Ghibou (named by me as a toddler, they asked me to name them and used the next two bits of babbling i made)
Rosie and Sarah (nothing super weird there)
Davina (feminized form of David) and Bextra (banned COX-2 inhibitor, a close cousin of Vioxx)
Ammonium Nitrate or Annie for sh…

Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning
Carlos Franzreb, Arnab Das, Tim Polzehl, Sebastian M\"oller
https://arxiv.org/abs/2508.09803

Improving the Speaker Anonymization Evaluation's Robustness to Target Speakers with Adversarial Learning
The current privacy evaluation for speaker anonymization often overestimates privacy when a same-gender target selection algorithm (TSA) is used, although this TSA leaks the speaker's gender and should hence be more vulnerable. We hypothesize that this occurs because the evaluation does not account for the fact that anonymized speech contains information from both the source and target speakers. To address this, we propose to add a target classifier that measures the influence of target speaker…

hah, can't fire me if i don't have a job, suckers! https://mastodon.social/@PatrickoftheG/115199991716282130

Hypothesis: Fediverse users have more capital/financial privileges than on other social media platforms. Let's test it!
How much cash (€) could you realistically make liquid within 1 year? Including selling car, flat, stocks etc. (excluding debts).
Anonymous & honest! (I'm having a discussion with a person from a marketing department...)
After voting, please boost/share to help get more responses! 😊

Bryce Young believers still sparse: NFL executive, coaches share anonymous opinions on Panthers QB

https://www.cbssports.com/nfl/news/bryce-y

Yes, please.
https://deanebarker.net/tech/blog/let-me-know/

The Missing Protocol: Let Me Know
I want a new protocol, tentatively called “Let Me Know” (LMK). The purpose is to provide someone an anonymous way to get notified when a singular, specific event occurs. Here’s a basic use case: Some random blog author has published Parts 1 and 2 of a series. You enjoyed it, and you want to know…

Wrote up a nice little macro in @… to illustrate "anonymous recursive functions" Š la PowerShell.
https://github.com/shriram/anonymous-r

GitHub - shriram/anonymous-recursive-function: Anonymous recursive functions in Racket
Anonymous recursive functions in Racket. Contribute to shriram/anonymous-recursive-function development by creating an account on GitHub.

Anwohnende kämpfen gegen Fußgängerüberweg vor einer Grundschule, weil wegen dem "ist Autofahren für die Anwohnenden kompliziert geworden". Gemeint ist: Sie dürfen aus ihrer privaten Einfahrt nicht mehr links abbiegen, sondern müssen 300m Umweg fahren.
https://www.s…

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Sri Harsha Gajavalli
https://arxiv.org/abs/2507.04104

Human-Centered Interactive Anonymization for Privacy-Preserving Machine Learning: A Case for Human-Guided k-Anonymity
Privacy-preserving machine learning (ML) seeks to balance data utility and privacy, especially as regulations like the GDPR mandate the anonymization of personal data for ML applications. Conventional anonymization approaches often reduce data utility due to indiscriminate generalization or suppression of data attributes. In this study, we propose an interactive approach that incorporates human input into the k-anonymization process, enabling domain experts to guide attribute preservation based…