Tag

#differential-privacy

Every item tagged differential-privacy, newest first.

5 items

A differentially private framework for gaining insights into AI chatbot use

Researchers at Google propose a differentially private framework for analyzing AI chatbot usage patterns while preserving user privacy. The framework enables insights into how users interact with chatbots without compromising individual data. This approach helps developers understand user behavior and improve chatbot design. Builders can apply similar techniques to their own analytics pipelines.

Key takeaways

Differential privacy preserves user data while providing usage insights.
Framework helps developers improve chatbot design.
Technique applicable to analytics pipelines.

GGoogle Research#differential-privacy #chatbot-analytics #privacy

researchNov 12

Differentially private machine learning at scale with JAX-Privacy

Google Research introduces JAX-Privacy, a library for differentially private machine learning at scale. JAX-Privacy integrates with JAX, enabling scalable and efficient training of private models. This library helps ensure the privacy of sensitive data used in machine learning models. You can use JAX-Privacy to train models that protect user data.

Key takeaways

JAX-Privacy is a library for differentially private machine learning.
It integrates with JAX for scalable and efficient model training.
JAX-Privacy helps protect user data in machine learning models.

GGoogle Research#differential-privacy #jax #machine-learning

researchOct 30

Toward provably private insights into AI use

Google Research proposes a framework for differentially private inference about AI usage patterns. The approach allows for provably private insights into how AI systems are used, without revealing individual user interactions. This development addresses growing concerns about AI privacy and could enable more transparent AI deployment. You can use this framework to assess AI usage in your applications while protecting user data.

Key takeaways

Differentially private inference framework for AI usage patterns.
Provably private insights without revealing individual interactions.
Enables transparent AI deployment with user data protection.

GGoogle Research#differential-privacy #ai-privacy #private-ai

modelsOct 23

VaultGemma: The world's most capable differentially private LLM

DeepMind introduces VaultGemma, a differentially private LLM trained from scratch. It outperforms prior private models on accuracy and efficiency. You can use VaultGemma for applications requiring high privacy standards. The model's performance sets a new benchmark for private LLMs.

Key takeaways

Trained from scratch with differential privacy.
Outperforms prior private models on accuracy and efficiency.
Sets a new benchmark for private LLMs.

DDeepMind#differential-privacy #private-llm #benchmarks

researchSep 12

VaultGemma: The world's most capable differentially private LLM

Google Research released VaultGemma, a differentially private LLM that outperforms prior private models on standard benchmarks. VaultGemma matches or exceeds the capabilities of non-private models like PaLM 2 and Gemma on many tasks. You can deploy VaultGemma for applications requiring high privacy and accuracy, such as healthcare and finance.

Key takeaways

Outperforms prior private LLMs on standard benchmarks.
Matches or exceeds non-private models like PaLM 2 and Gemma.
Deployable for high-privacy applications like healthcare.

GGoogle Research#differential-privacy #private-llm #benchmarks