#differentialprivacy — Public Fediverse posts on home.social

PrivacyDigest @[email protected] · 2025-10-29 · 11:12 UTC

The #Republican Plan to Reform the #Census Could Put Everyone’s #Privacy at Risk

A little-known #algorithmic process called “differential privacy” helps keep census data #anonymous. Conservatives want it gone.
#differentialprivacy #algorithms

https://www.wired.com/story/republicans-differential-privacy-census-overhaul/

#algorithms #differentialprivacy #anonymous #algorithmic #privacy #census

apfeltalk :verified: @[email protected] · 2025-05-19 · 09:00 UTC

Apple überarbeitet Siri: „LLM Siri“ soll Neuanfang bringen
Apple steht vor einem grundlegenden Umbau seines Sprachassistenten Siri. Ziel ist eine neue, KI-basierte Version namens „LLM Siri“. Diese Entwicklung folgt auf Problem
https://www.apfeltalk.de/magazin/news/apple-ueberarbeitet-siri-llm-siri-soll-neuanfang-bringen/
#KI #News #AppleAIStrategie #AppleIntelligence #AppleSprachassistent #DifferentialPrivacy #GenerativeKI #JohnGiannandrea #KIApple #LLMSiri #PerplexityAI #SiriUpdate

#ki #news #appleaistrategie #appleintelligence #applesprachassistent #differentialprivacy

Miguel Afonso Caetano @[email protected] · 2025-01-15 · 19:52 UTC

"To prevent AI models from memorizing their input, we know exactly one robust method: differential privacy (DP). But crucially, DP requires you to precisely define what you want to protect. For example, to protect individual people, you must know which piece of data comes from which person in your dataset. If you have a dataset with identifiers, that's easy. If you want to use a humongous pile of data crawled from the open Web, that's not just hard: that's fundamentally impossible.

In practice, this means that for massive AI models, you can't really protect the massive pile of training data. This probably doesn't matter to you: chances are, you can't afford to train one from scratch anyway. But you may want to use sensitive data to fine-tune them, so they can perform better on some task. There, you may be able to use DP to mitigate the memorization risks on your sensitive data.

This still requires you to be OK with the inherent risk of the off-the-shelf LLMs, whose privacy and compliance story boils down to "everyone else is doing it, so it's probably fine?".

To avoid this last problem, and get robust protection, and probably get better results… Why not train a reasonably-sized model entirely on data that you fully understand instead?"

https://desfontain.es/blog/privacy-in-ai.html

#AI #GenerativeAI #LLMs #SLMs #Privacy #DifferentialPrivacy #Memorization