Tootfinder

"#Kagi is a heavily customized, anonymized front-end to Google.
The implications of this are stunning. It means that Google's enshittified search-results are a choice. Those ad-strewn, sub-Altavista, spam-drowned #search pages are a feature, not a bug. Google prefers those to Kagi, because Google…

A photo hasn't to be crisp and pin sharp. Also the photo can be boring and the location unoccupied. Even location is no must. Oberservational voyeuristic street #photography in the streets of the big city might be easier than in small cities because anonymity is not there, huge busy crowds are not there in empty streets. One can not hide in the crowd averting attention dancing like a

The VoicePrivacy 2024 Challenge Evaluation Plan
Natalia Tomashenko, Xiaoxiao Miao, Pierre Champion, Sarina Meyer, Xin Wang, Emmanuel Vincent, Michele Panariello, Nicholas Evans, Junichi Yamagishi, Massimiliano Todisco
https://arxiv.org/abs/2404.02677

The VoicePrivacy 2024 Challenge Evaluation Plan
The task of the challenge is to develop a voice anonymization system for speech data which conceals the speaker's voice identity while protecting linguistic content and emotional states. The organizers provide development and evaluation datasets and evaluation scripts, as well as baseline anonymization systems and a list of training resources formed on the basis of the participants' requests. Participants apply their developed anonymization systems, run evaluation scripts and submit evaluation …

This https://arxiv.org/abs/2008.10771 has been replaced.
link: https://scholar.google.com/scholar?q=a

MuCo: Publishing Microdata with Privacy Preservation through Mutual Cover
We study the anonymization technique of k-anonymity family for preserving privacy in the publication of microdata. Although existing approaches based on generalization can provide good enough protections, the generalized table always suffers from considerable information loss, mainly because the distributions of QI (Quasi-Identifier) values are barely preserved and the results of query statements are groups rather than specific tuples. To this end, we propose a novel technique, called the Mutua…

Via Vincent Proce on Twitter on AI:
“This is making the rounds!” (On #AIArt)

"Pitchfork will continue operating its own website instead of transitioning into the GQ domain." 😃
There's an interesting quote in here that got me thinking. It says Pitchfork has the highest amount of site visitors compared to other Condé sites. Technically this is untrue.
But the point remains because their page view volume is #1, and their Visit Duration is by far the highest. That's really valuable.
The only metric I can think might be lower on Pitc…

Pitchfork website will remain, staff say
Speaking anonymously to RA, employees confirmed the publication won't lose its digital footprint.

Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning
Kaiping Cui, Xia Feng, Liangmin Wang, Haiqin Wu, Xiaoyu Zhang, Boris D\"udder
https://arxiv.org/abs/2402.15111

Chu-ko-nu: A Reliable, Efficient, and Anonymously Authentication-Enabled Realization for Multi-Round Secure Aggregation in Federated Learning
Secure aggregation enables federated learning (FL) to perform collaborative training of clients from local gradient updates without exposing raw data. However, existing secure aggregation schemes inevitably perform an expensive fresh setup per round because each client needs to establish fresh input-independent secrets over different rounds. The latest research, Flamingo (S&P 2023), designed a share-transfer-based reusable secret key to support the server continuously performing multiple rounds…

Confidential Federated Computations
Hubert Eichner, Daniel Ramage, Kallista Bonawitz, Dzmitry Huba, Tiziano Santoro, Brett McLarnon, Timon Van Overveldt, Nova Fallen, Peter Kairouz, Albert Cheu, Katharine Daly, Adria Gascon, Marco Gruteser, Brendan McMahan
https://arxiv.org/abs/2404.10764

Confidential Federated Computations
Federated Learning and Analytics (FLA) have seen widespread adoption by technology platforms for processing sensitive on-device data. However, basic FLA systems have privacy limitations: they do not necessarily require anonymization mechanisms like differential privacy (DP), and provide limited protections against a potentially malicious service provider. Adding DP to a basic FLA system currently requires either adding excessive noise to each device's updates, or assuming an honest service prov…