@michele-miranda.bsky.social
This work is a collaboration with
@esruzzetti.bsky.social , @asantilli.bsky.social, @fmz8.bsky.social , Sébastien Bratières, and
Emanuele Rodolà. 🙌 It's part of the EU project DataTools4Heart joined by Translated.
@esruzzetti.bsky.social , @asantilli.bsky.social, @fmz8.bsky.social , Sébastien Bratières, and
Emanuele Rodolà. 🙌 It's part of the EU project DataTools4Heart joined by Translated.
February 12, 2025 at 4:19 PM
This work is a collaboration with
@esruzzetti.bsky.social , @asantilli.bsky.social, @fmz8.bsky.social , Sébastien Bratières, and
Emanuele Rodolà. 🙌 It's part of the EU project DataTools4Heart joined by Translated.
@esruzzetti.bsky.social , @asantilli.bsky.social, @fmz8.bsky.social , Sébastien Bratières, and
Emanuele Rodolà. 🙌 It's part of the EU project DataTools4Heart joined by Translated.
📖 The core approaches discussed in our survey are available in this GitHub repository:
github.com/michele17284...
github.com/michele17284...
GitHub - michele17284/Awesome-Privacy-Preserving-LLMs: Collection of all the papers talking about/relevant to the topic of privacy-preserving LLMs
Collection of all the papers talking about/relevant to the topic of privacy-preserving LLMs - michele17284/Awesome-Privacy-Preserving-LLMs
github.com
February 12, 2025 at 4:19 PM
📖 The core approaches discussed in our survey are available in this GitHub repository:
github.com/michele17284...
github.com/michele17284...
We also review available libraries to implement these privacy mechanisms into models.
February 12, 2025 at 4:19 PM
We also review available libraries to implement these privacy mechanisms into models.
To address these challenges, we explore comprehensive solutions for integrating privacy mechanisms throughout the learning pipeline—from data anonymization and differential privacy to machine unlearning techniques. 🛡️
February 12, 2025 at 4:19 PM
To address these challenges, we explore comprehensive solutions for integrating privacy mechanisms throughout the learning pipeline—from data anonymization and differential privacy to machine unlearning techniques. 🛡️
We examine threats by reviewing privacy attacks on LLMs such as Training Data Extraction, Membership Inference, and Model Inversion, along with their implications. ⚠️
February 12, 2025 at 4:19 PM
We examine threats by reviewing privacy attacks on LLMs such as Training Data Extraction, Membership Inference, and Model Inversion, along with their implications. ⚠️
Large Language Models are incredibly powerful but pose significant privacy risks when trained on private or sensitive data—especially in critical domains like healthcare🏥.
February 12, 2025 at 4:19 PM
Large Language Models are incredibly powerful but pose significant privacy risks when trained on private or sensitive data—especially in critical domains like healthcare🏥.