Tootfinder

Broncos player had multiple cars stolen from team hotel after he made questionable decision in parking lot

https://www.cbssports.com/nfl/news/b…

Broncos player had multiple cars stolen from team hotel after he made questionable decision in parking lot
Anonymous player left key fob in his vehicle

So, I've been recently having a conversation with several folk about Slack vs Discord and which platform may be more advantagous for organizing events and building community. I and others were surprised to learn of how wide the gulf is on various people's ranges of experiences on each service, in terms of community, harassment, security tools, and users feeling welcomed.
I recognize that my experience as a femme-presenting person who uses my real identity is different from that of an anonymous person with a profile photo showing a bearded guy in a Patagonia vest holding up an IPA. This tracks with the discussions I've had today: tech bros generally seem to lean toward one platform while all of my other business-owning women friends opt to steer away from that service almost entirely.
If you're femme and use your real identity online, do you have a preference for Slack or Discord? Have you ever opted out of using either Slack or Discord? In addition to discussion of general vibes and security tools, we also can all recognize that there are some surveillance/privacy tradeoffs in play, as well. (Just as, if you're an organizer, maybe you have thoughts about which service offers tools or features or pricing that steers you one way or another, as well?)
What are your stories on this topic?

This https://arxiv.org/abs/2308.04455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal…

This https://arxiv.org/abs/2308.04455 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCR_…

Anonymizing Speech: Evaluating and Designing Speaker Anonymization Techniques
The growing use of voice user interfaces has led to a surge in the collection and storage of speech data. While data collection allows for the development of efficient tools powering most speech services, it also poses serious privacy issues for users as centralized storage makes private personal speech data vulnerable to cyber threats. With the increasing use of voice-based digital assistants like Amazon's Alexa, Google's Home, and Apple's Siri, and with the increasing ease with which personal…

I had the bad luck to oversee a piece of the Bill Maher show last night. That guy has perfected stupidity. And worse, he is slowly becoming a maga apologist.
Last night he was bloviating about how we needed age based limits on cell phone usage. Yet he utterly does not recognize that the checking of age is a hoop that every person will have to jump through - and as a side effect that will damage, perhaps destroy, anonymous or disguised speech, a thing we have long found to be a benef…

Click-trackers in email "need" to rewrite rewritten links in order to properly determine who clicked it. In longer threads, this gets a bit hilarious.
Here's the final payload at the end of an 18,660-character re-re-re-…-rewrite:
http%25252525252525252525252525252525252525252525252525252525252525253A%25252525252525252525252525252525252525252525252525252525252525252F%25252525252525252525252525252525252525252525252525252525252525252Fwww.example.com%25252525252525252525252525252525252525252525252525252525252525252F
(anonymized)
URI percent encoding uses percents, so : is : and / is /. All of those 25s are from escaped percents; % is %, so unescape(unescape(unescape("%253A"))) gives you :.
That's three layers. The above example has 34 layers.

Identifying Multiple Personalities in Large Language Models with External Evaluation
Xiaoyang Song, Yuta Adachi, Jessie Feng, Mouwei Lin, Linhao Yu, Frank Li, Akshat Gupta, Gopala Anumanchipalli, Simerjot Kaur
https://arxiv.org/abs/2402.14805

Identifying Multiple Personalities in Large Language Models with External Evaluation
As Large Language Models (LLMs) are integrated with human daily applications rapidly, many societal and ethical concerns are raised regarding the behavior of LLMs. One of the ways to comprehend LLMs' behavior is to analyze their personalities. Many recent studies quantify LLMs' personalities using self-assessment tests that are created for humans. Yet many critiques question the applicability and reliability of these self-assessment tests when applied to LLMs. In this paper, we investigate LLM …

This https://arxiv.org/abs/2305.08460 has been replaced.
link: https://scholar.google.com/scholar?q=a

Selective Population Protocols
The model of population protocols provides a universal platform to study distributed processes driven by pairwise interactions of anonymous agents. While population protocols present an elegant and robust model for randomized distributed computation, their efficiency wanes when tackling issues that require more focused communication or the execution of multiple processes. To address this issue, we propose a new, selective variant of population protocols by introducing a partition of the state s…

TrustRate: A Decentralized Platform for Hijack-Resistant Anonymous Reviews
Rohit Dwivedula, Sriram Sridhar, Sambhav Satija, Muthian Sivathanu, Nishanth Chandran, Divya Gupta, Satya Lokam
https://arxiv.org/abs/2402.18386

TrustRate: A Decentralized Platform for Hijack-Resistant Anonymous Reviews
Reviews and ratings by users form a central component in several widely used products today (e.g., product reviews, ratings of online content, etc.), but today's platforms for managing such reviews are ad-hoc and vulnerable to various forms of tampering and hijack by fake reviews either by bots or motivated paid workers. We define a new metric called 'hijack-resistance' for such review platforms, and then present TrustRate, an end-to-end decentralized, hijack-resistant platform for authentic, a…

Large Language Models are Advanced Anonymizers
Robin Staab, Mark Vero, Mislav Balunovi\'c, Martin Vechev
https://arxiv.org/abs/2402.13846 https://

Large Language Models are Advanced Anonymizers
Recent work in privacy research on large language models has shown that they achieve near human-level performance at inferring personal data from real-world online texts. With consistently increasing model capabilities, existing text anonymization methods are currently lacking behind regulatory requirements and adversarial threats. This raises the question of how individuals can effectively protect their personal data in sharing online texts. In this work, we take two steps to answer this quest…